[
{
"path": ".gitignore",
"content": "outputs/*\n*.pyc\n"
},
{
"path": "LICENSE",
"content": " LOST\n \n Copyright 2021 Valeo\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n https://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n \n \n \n Apache License\n Version 2.0, January 2004\n https://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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},
{
"path": "README.md",
"content": "# LOST \r\nPytorch implementation of the unsupervised object discovery method **LOST**. More details can be found in the paper:\r\n\r\n**Localizing Objects with Self-Supervised Transformers and no Labels**, BMVC 2021 [[arXiv](https://arxiv.org/abs/2109.14279)] \r\nby *Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet and Jean Ponce*\r\n\r\n \r\n\r\n\r\n\\\r\nIf you use the **LOST** code or framework in your research, please consider citing:\r\n\r\n\r\n```\r\n@inproceedings{LOST,\r\n title = {Localizing Objects with Self-Supervised Transformers and no Labels},\r\n author = {Oriane Sim\\'eoni and Gilles Puy and Huy V. Vo and Simon Roburin and Spyros Gidaris and Andrei Bursuc and Patrick P\\'erez and Renaud Marlet and Jean Ponce},\r\n journal = {Proceedings of the British Machine Vision Conference (BMVC)},\r\n month = {November},\r\n year = {2021}\r\n}\r\n```\r\n\r\n## Content\r\n#### LOST\r\n- [Installation of LOST](#installation-of-lost) \r\n- [Apply LOST to one image](#apply-lost-to-one-image)\r\n- [Launching LOST on datasets](#launching-lost-on-datasets)\r\n\r\n#### Towards unsupervised object detection\r\n- [Installation of LOST+CAD/OD](#installation-for-cad-and-od-trainings)\r\n- [Training LOST+CAD](#training-a-class-agnostic-detector-cad-with-lost-pseudo-annotations)\r\n- [Training LOST+OD](#training-a-class-aware-detector-od-with-lost-pseudo-annotations)\r\n\r\n\r\n## Installation of LOST\r\n### Dependencies\r\n\r\nThis code was implemented with python 3.7, PyTorch 1.7.1 and CUDA 10.2. Please install [PyTorch](https://pytorch.org/). In order to install the additionnal dependencies, please launch the following command:\r\n\r\n```\r\npip install -r requirements.txt\r\n```\r\n\r\n### Install DINO\r\nThis method is based on DINO [paper](https://arxiv.org/pdf/2104.14294.pdf). The framework can be installed using the following commands:\r\n```\r\ngit clone https://github.com/facebookresearch/dino.git\r\ncd dino; \r\ntouch __init__.py\r\necho -e \"import sys\\nfrom os.path import dirname, join\\nsys.path.insert(0, join(dirname(__file__), '.'))\" >> __init__.py; cd ../;\r\n```\r\n\r\nThe code was made using the commit ba9edd1 of DINO repo (please rebase if breakage).\r\n\r\n## Apply LOST to one image\r\nFollowing are scripts to apply LOST to an image defined via the `image_path` parameter and visualize the predictions (`pred`), the maps of the Figure 2 in the paper (`fms`) and the visulization of the seed expansion (`seed_expansion`). Box predictions are also stored in the output directory given by parameter `output_dir`.\r\n\r\n```\r\npython main_lost.py --image_path examples/VOC07_000236.jpg --visualize pred\r\npython main_lost.py --image_path examples/VOC07_000236.jpg --visualize fms\r\npython main_lost.py --image_path examples/VOC07_000236.jpg --visualize seed_expansion\r\n```\r\n\r\n## Launching LOST on datasets\r\nFollowing are the different steps to reproduce the results of **LOST** presented in the paper. \r\n\r\n### PASCAL-VOC\r\nPlease download the PASCAL VOC07 and PASCAL VOC12 datasets ([link](http://host.robots.ox.ac.uk/pascal/VOC/)) and put the data in the folder `datasets`. There should be the two subfolders: `datasets/VOC2007` and `datasets/VOC2012`. In order to apply lost and compute corloc results (VOC07 61.9, VOC12 64.0), please launch:\r\n```\r\npython main_lost.py --dataset VOC07 --set trainval\r\npython main_lost.py --dataset VOC12 --set trainval\r\n```\r\n\r\n### COCO\r\nPlease download the [COCO dataset](https://cocodataset.org/#home) and put the data in `datasets/COCO`. Results are provided given the 2014 annotations following previous works. The following command line allows you to get results on the subset of 20k images of the COCO dataset (corloc 50.7), following previous litterature. To be noted that the 20k images are a subset of the `train` set.\r\n```\r\npython main_lost.py --dataset COCO20k --set train\r\n```\r\n\r\n### Different models\r\nWe have tested the method on different setups of the VIT model, corloc results are presented in the following table (more can be found in the paper). \r\n\r\n\r\n \r\n | arch | \r\n pre-training | \r\n dataset | \r\n
\r\n \r\n | \r\n | \r\n VOC07 | \r\n VOC12 | \r\n COCO20k | \r\n
\r\n \r\n | ViT-S/16 | \r\n DINO | \r\n 61.9 | \r\n 64.0 | \r\n 50.7 | \r\n
\r\n
\r\n | ViT-S/8 | \r\n DINO | \r\n 55.5 | \r\n 57.0 | \r\n 49.5 | \r\n
\r\n
\r\n | ViT-B/16 | \r\n DINO | \r\n 60.1 | \r\n 63.3 | \r\n 50.0 | \r\n
\r\n
\r\n | ResNet50 | \r\n DINO | \r\n 36.8 | \r\n 42.7 | \r\n 26.5 | \r\n
\r\n
\r\n | ResNet50 | \r\n Imagenet | \r\n 33.5 | \r\n 39.1 | \r\n 25.5 | \r\n
\r\n
\r\n\r\n\\\r\nPrevious results on the dataset `VOC07` can be obtained by launching: \r\n```\r\npython main_lost.py --dataset VOC07 --set trainval #VIT-S/16\r\npython main_lost.py --dataset VOC07 --set trainval --patch_size 8 #VIT-S/8\r\npython main_lost.py --dataset VOC07 --set trainval --arch vit_base #VIT-B/16\r\npython main_lost.py --dataset VOC07 --set trainval --arch resnet50 #Resnet50/DINO\r\npython main_lost.py --dataset VOC07 --set trainval --arch resnet50_imagenet #Resnet50/imagenet\r\n```\r\n\r\n## Towards unsupervised object detection\r\nIn this work, we additionally use LOST predictions to train object detection models without any human supervision. We explore two scenarios: class-agnostic (CAD) and (pseudo) class-aware training of object detectors (OD). The next section present the different steps to reproduce our results.\r\n\r\n### Installation for CAD and OD trainings\r\nWe use the [detectron2](https://github.com/facebookresearch/detectron2) framework to train a Faster R-CNN model with LOST predictions as pseudo-gt. The code was developped with the version [v0.5](https://github.com/facebookresearch/detectron2/releases) of the framework. In order to reproduce our results, please install detectron2 using the next commands. In case of failure, you can find the installation corresponding to your version of pytorch/CUDA [here](https://github.com/facebookresearch/detectron2/releases).\r\n```bash\r\ngit clone https://github.com/facebookresearch/detectron2.git\r\npython -m pip install detectron2==0.5\r\n```\r\n\r\nSet global variables for ease of usage. \r\n```bash\r\nexport LOST=$(pwd)\r\ncd detectron2; export D2=$(pwd);\r\n```\r\n\r\nThen please copy LOST-specific files to detectron2 framework, following:\r\n```bash\r\nln -s $LOST/tools/*.py $D2/tools/. # Move LOST tools to D2\r\nmkdir $D2/configs/LOST\r\nln -s $LOST/tools/configs/* $D2/configs/LOST/. # Move LOST configs to D2\r\n```\r\n\r\n### Training a Class-Agnostic Detector (CAD) with LOST pseudo-annotations\r\n\r\nBefore launching a training, data must be formated to fit detectron2 and COCO styles. Following are the command lines to do this formatting for boxes predicted with LOST.\r\n```bash\r\ncd $D2; \r\n\r\n# Format DINO weights to fit detectron2\r\nwget https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth -P ./data # Download the model from DINO\r\npython tools/convert_pretrained_to_detectron_format.py --input ./data/dino_resnet50_pretrain.pth --output ./data/dino_RN50_pretrain_d2_format.pkl\r\n\r\n# Format pseudo-boxes data to fit detectron2\r\npython tools/prepare_voc_LOST_CAD_pseudo_boxes_in_detectron2_format.py --year 2007 --pboxes $LOST/data/LOST_predictions/LOST_VOC07.pkl\r\n\r\n# Format VOC data to fit COCO style\r\npython tools/prepare_voc_data_in_coco_style.py --is_CAD --voc07_dir $LOST/datasets/VOC2007 --voc12_dir $LOST/datasets/VOC2012\r\n```\r\n\r\nThe next command line allows you to launch a CAD training with 4 gpus on the VOC2007 dataset. The batch size is set to 16, 4 to 8 GPUs may be needed depending on your machines. Please make sure to change the argument value `MODEL.WEIGHTS` to the correct path of DINO weights.\r\n```bash\r\npython tools/train_net_for_LOST_CAD.py --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_VOC07_CAD.yaml DATALOADER.NUM_WORKERS 8 OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_VOC07_CAD MODEL.WEIGHTS ./data/dino_RN50_pretrain_d2_format.pkl\r\n```\r\n\r\nInference results of the model will be stored in `$OUTPUT_DIR/inference`. In order to produce results on the `train+val` dataset, please use the following command:\r\n```\r\npython tools/train_net_for_LOST_CAD.py --resume --eval-only --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_VOC07_CAD.yaml DATALOADER.NUM_WORKERS 6 MODEL.WEIGHTS ./outputs/RN50_DINO_FRCNN_VOC07_CAD/model_final.pth OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_VOC07_CAD/ DATASETS.TEST '(\"voc_2007_trainval_CAD_coco_style\", )'\r\ncd $LOST;\r\npython main_corloc_evaluation.py --dataset VOC07 --set trainval --type_pred detectron --pred_file $D2/outputs/RN50_DINO_FRCNN_VOC07_CAD/inference/coco_instances_results.json\r\n```\r\n\r\n#### Training LOST+CAD on COCO20k dataset\r\nFollowing are the command lines allowing to train a detector in a class-agnostic fashion on the COCO20k subset of COCO dataset.\r\n\r\n```bash\r\ncd $D2;\r\n\r\n# Format pseudo-boxes data to fit detectron2\r\npython tools/prepare_coco_LOST_CAD_pseudo_boxes_in_detectron2_format.py --pboxes $LOST/outputs/COCO20k_train/LOST-vit_small16_k/preds.pkl\r\n\r\n# Generate COCO20k CAD gt annotations\r\npython tools/prepare_coco_CAD_gt.py --coco_dir $LOST/datasets/COCO\r\n\r\n# Train detector (evaluation done on COCO20k CAD training set)\r\npython tools/train_net_for_LOST_CAD.py --num-gpus 4 --config-file ./configs/LOST/RN50_DINO_FRCNN_COCO20k_CAD.yaml DATALOADER.NUM_WORKERS 8 OUTPUT_DIR ./outputs/RN50_DINO_FRCNN_COCO20k_CAD MODEL.WEIGHTS ./data/dino_RN50_pretrain_d2_format.pkl\r\n\r\n# Corloc evaluation\r\npython main_corloc_evaluation.py --dataset COCO20k --type_pred detectron --pred_file $D2/outputs/RN50_DINO_FRCNN_COCO20k_CAD/inference/coco_instances_results.json\r\n```\r\n\r\n\r\n#### Evaluating LOST+CAD (corloc results)\r\n\r\nWe have provided predictions of a class-agnostic Faster R-CNN model trained using LOST boxes as pseudo-gt; they are stored in the folder `data/CAD_predictions`. In order to launch the corloc evaluation, please launch the following scripts. It is to be noted that in this evaluation, only the box with the highest confidence score is considered per image. \r\n\r\n```bash\r\npython main_corloc_evaluation.py --dataset VOC07 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC07.json\r\npython main_corloc_evaluation.py --dataset VOC12 --set trainval --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_VOC12.json\r\npython main_corloc_evaluation.py --dataset COCO20k --set train --type_pred detectron --pred_file data/CAD_predictions/LOST_plus_CAD_COCO20k.json\r\n```\r\n\r\nThe following table presents the obtained corloc results.\r\n\r\n\r\n \r\n | method | \r\n dataset | \r\n
\r\n \r\n | \r\n VOC07 | \r\n VOC12 | \r\n COCO20k | \r\n
\r\n \r\n | LOST | \r\n 61.9 | \r\n 64.0 | \r\n 50.7 | \r\n
\r\n
\r\n | LOST+CAD | \r\n 65.7 | \r\n 70.4 | \r\n 57.5 | \r\n
\r\n
![\"LOST](\"examples/LOST_ex0.png\")
\r\n ![\"LOST](\"examples/LOST_ex1.png\")
\r\n ![\"LOST](\"examples/LOST_ex2.png\")
\r\n