.
================================================
FILE: MANIFEST.in
================================================
recursive-include . *
================================================
FILE: README.md
================================================
Data Labeling, Tracking and Annotation with AI
DLTA-AI is the next generation of annotation tools, integrating the power of Computer Vision SOTA models to
Labelme in a seamless expirence and intuitive workflow to make creating image datasets easier than ever before
[](https://0ssamaak0.github.io/DLTA-AI/)
[](https://www.youtube.com/@DLTA-AI)
[](https://discord.gg/9Rmwcnc4)
[](https://pypi.org/project/DLTA-AI/)
[](https://github.com/0ssamaak0/DLTA-AI/releases)
[](https://github.com/0ssamaak0/DLTA-AI/issues)
[](https://github.com/0ssamaak0/DLTA-AI/commits)
[](https://github.com/0ssamaak0/DLTA-AI/blob/master/LICENSE)
[Installation](#installation-%EF%B8%8F) 🛠️ | [Segment Anything](#Segment-Anything-) 🪄 | [Model Selection](#model-selection-) 🤖 | [Segmentation](#segmentation-) 🎨 | [Object Tracking](#object-tracking-) 🚗 | [Export](#export-) 📤 | [Other Features](#other-features-) 🌟| [Contributing](#contributing-) 🤝| [Acknowledgements](#acknowledgements-)🙏| [Resources](#resources-) 🌐 | [License](#license-) 📜
# Installation 🛠️
After creating a new environment, installing Pytorch to it, you can install DLTA-AI using pip
```
pip install DLTA-AI
```
and run it using
```
DLTA-AI
```
Check the [Installation section in User Guide](https://0ssamaak0.github.io/DLTA-AI/installation/full-installation/) for more details, different installation options and solutions for common issues.
# Segment Anything 🪄
DLTA-AI takes the Annotation to the next level by integrating lastest Meta models [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything) to support zero-shot segmentation for any class
**SAM** can be used also to improve the quality of Segmentation, even inaccurate polygons around the object is enough to be segmented correctly
**SAM** doesn't only work for Segmentation tasks, it's build in the video mode to support **Object Tracking** as well for any class
# Model Selection 🤖
For model selection, DLTA-AI provides the **Model Explorer** to utilize the power of the numerous models in [mmdetection](https://github.com/open-mmlab/mmdetection/tree/2.x) and [ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) as well as the models of [SAM](https://github.com/facebookresearch/segment-anything)
the to give the user the ability to compare, download and select from the library of models
# Segmentation 🎨
Using the models from the **Model Explorer**, DLTA-AI provides a seamless expirence to annotate single image or batch of images, with options to select classes, modify threshold, and full control to edit the segmentation results.
and as mentioned before, **SAM** is fully integrated in DLTA-AI to provide zero-shot segmentation for any class, and to improve the quality of segmentation
# Object Tracking 🚗
Built on top of the segmentation and detection models, DLTA-AI provides a complete solution for Object Tracking, with 5 different models for tracking
To import DLTA-AI have options for video navigation, tracking settings and different visualization options with the ability to export the tracking results to a video file
Beside this, DLTA-AI provides a completely new way to modify the tracking results, including edit and delete propagation across frames
Beside automatic tracking models, DLTA-AI provides different methods of interpolation and filling gaps between frames to fix occlusions and unpredicted behaviors in a semi-automatic way
# Export 📤
For Instance Segmentation, DLTA-AI provides to option to export the segmentation to standard COCO format, and the results of tracking to MOT format, and a video file for the tracking results with desired visualization options e.g., show id, bbox, class name, etc.
DLTA-AI provides also the ability to add user-defined or custom export formats that can be used for any purpose, once the user defines his own format, it will be available in the export menu.
# Other Features 🌟
- Threshold Selection (Confidence and IoU)
- Select Classes (from 80 COCO classes) with option to save default classes
- Track assigned objects only
- Merging models (Run both models and merge the results)
- Show Runtime Type (CPU/GPU)
- Show GPU Memory Usage
- Video Navigation (Frame by Frame, Fast Forward, Fast Backward, Play/Pause)
- Light / Dark Theme Support (syncs with OS theme)
- Fully Customizable UI (drag and drop, show/hide)
- OS Notifications (for long running tasks)
- using orjson for faster json serialization
- additional script (external) to evaluate the results of segmentation (COCO)
- additional script (external) to extract frames from a video file for future use
- User shortcuts and preferences settings
# Contributing 🤝
DLTA-AI is an open source project and contributions are very welcome, specially in this early stage of development.
You can contribute in many ways:
- Create an [issue](https://github.com/0ssamaak0/DLTA-AI/issues) Reporting bugs 🐞 or suggesting new features 🌟 or just give your feedback 📝
- Create a [pull request](https://github.com/0ssamaak0/DLTA-AI/pulls) to fix bugs or add new features, or just to improve the code quality, optimize performance, documentation, or even just to fix typos
- Review [pull requests](https://github.com/0ssamaak0/DLTA-AI/pulls) and help with the code review process
- Spread the word about DLTA-AI and help us grow the community 🌎, by sharing the project on social media, or just by telling your friends about it
# Acknowledgements 🙏
This tool is part of a Graduation Project at [Faculty of Engineering, Ain Shams University](https://eng.asu.edu.eg/) under the supervision of:
- [Dr. Karim Ismail](https://carleton.ca/cee/profile/karim-ismail/)
- [Dr. Ahmed Osama](ahmed.osama@eng.asu.edu.eg)
- Dr. Watheq El-Kharashy
- [Eng. Yousra El-Qattan](https://www.linkedin.com/in/youssra-elqattan/)
we want also to thank our friends who helped us with testing, feedback and suggestions:
- [Eng. Houssam Siyoufi](https://www.linkedin.com/in/houssam-siyoufi-163627110/)
- [Amin Mohamed](https://github.com/AminMohamed-3)
- [Badr Mohamed](https://github.com/Badr-1)
- [Ahmed Mahmoud](https://github.com/ahmedd-mahmoudd)
- [Youssef Ashraf](https://github.com/0xNine9)
- [Chadi Ashraf](https://github.com/Chady00)
# Resources 🌐
- [Labelme](https://github.com/wkentaro/labelme)
- [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything)
- [MMDetection](https://github.com/open-mmlab/mmdetection/tree/2.x)
- [ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
- [mikelbrostrom yolov8_tracking](https://github.com/mikel-brostrom/yolov8_tracking)
- [orjson](https://github.com/ijl/orjson)
- [icons8](https://icons8.com/)
# License 📜
DLTA-AI is released under the [GPLv3 license](https://github.com/0ssamaak0/DLTA-AI/blob/master/LICENSE).
================================================
FILE: additional_scripts/coco_eval.py
================================================
import sys
import os
import json
import argparse
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# Define a class to suppress print statements
class HiddenPrints:
"""
A context manager to suppress print statements.
"""
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
# Define the function to evaluate coco
def evaluate_coco(gt_file: str, pred_file: str, task: str = "bbox", evaluation_type: str = "full") -> None:
"""
Evaluates the performance of a COCO object detection model.
Args:
gt_file (str): Path to the ground truth file.
pred_file (str): Path to the prediction file.
task (str, optional): The type of task to evaluate (bbox or segm). Defaults to "bbox".
evaluation_type (str, optional): The type of evaluation to perform (full or mAP). Defaults to "full".
"""
# Use HiddenPrints to suppress print statements
with HiddenPrints():
# Load the ground truth file
coco_gt = COCO(gt_file)
# Load the prediction file
with open(pred_file, 'r') as f:
pred_file = json.load(f)
pred_file = pred_file[0]['annotations'] # type: ignore
coco_dt = coco_gt.loadRes(pred_file)
# Create a COCO evaluator object
coco_eval = COCOeval(coco_gt, coco_dt, task) # type: ignore
# Evaluate the model
coco_eval.evaluate()
coco_eval.accumulate()
# Compute stats
coco_eval.summarize()
# Print the results based on the evaluation type
if evaluation_type == "full":
coco_eval.summarize()
elif evaluation_type == "mAP":
print(f"{task} mAP: {coco_eval.stats[0]:.3f}")
# Create an argument parser
parser = argparse.ArgumentParser()
parser.add_argument("--gt_file", required=True, help="ground truth file")
parser.add_argument("--pred_file", required=True, help="prediction file")
parser.add_argument("--task", default="bbox",
choices=["bbox", "segm"], help="task (bbox or segm)")
parser.add_argument("--evaluation_type", default="full",
choices=["full", "mAP"], help="evaluation type (full or mAP)")
# Parse the arguments
args = parser.parse_args()
# Run the function with the arguments
evaluate_coco(args.gt_file, args.pred_file, args.task, args.evaluation_type)
================================================
FILE: docs/Installation/executable.md
================================================
---
label: Executable (CPU Only)
order: 2
icon: ":package:"
---
DLTA-AI is available as an executable, however it's CPU only, so it's not recommended for large datasets. It's currently available for windows and linux only
you can download the [lastest release](https://github.com/0ssamaak0/DLTA-AI/releases) Executable under **Assets**
The Executable doesn't require any installation, just download and run it from the executable file
================================================
FILE: docs/Installation/full installation.md
================================================
---
label: full installation
icon: ":inbox_tray:"
order: 3
---
# Full Installation
## Create a Virtual Environment
It is highly recommended to install DTLA-AI in virtual environment using conda. This will ensure a clean and isolated environment for the installation process. use `python=3.8` to avoid any compatibility issues
```
conda create -n DLTA-AI python=3.8
conda activate DLTA-AI
```
## Install Pytorch
First, you need to install [pytorch](https://pytorch.org/get-started/locally/) according to your device and your OS, if you have GPU, choose CUDA version, otherwise choose CPU version
Example:
```
conda install pytorch torchvision torchaudio .... -c pytorch>
```
!!!
Due to inconsistency between the current versions of `pytorch` and `mmcv`, some users may face issues when using `pytorch 2.x`
if you face any issues, try to install `pytorch 1.13.1` instead
!!!
## Option 1: Using pip
Installation using pip is more easier since it handles all dependencies
```
pip install DLTA-AI
```
then run it from anywhere using
```
DLTA-AI
```
note that first time running DLTA-AI, it will download a required module, it may take some time
you can also use pip for updating DLTA-AI
```
pip install DLTA-AI -U
```
## Option 2: Manual Installation
Download the lastest release from [here](https://github.com/0ssamaak0/DLTA-AI/releases)
install requirements
```
pip install -r requirements.txt
mim install mmcv-full==1.7.0
```
then
Run the tool from `DLTA_AI_app` directory
```
cd DLTA_AI_app
python __main__.py
```
================================================
FILE: docs/Installation/index.yml
================================================
label: Installation
icon: ":hammer_and_wrench:"
expanded: true
order: 50
================================================
FILE: docs/Installation/problems.md
================================================
---
label: possible problems
order: 1
icon: ":interrobang:"
---
# Solutions to possible problems
## Qt Platform Plugin Error in OpenCV on Linux Machines 🐧
some linux machines may have this problem
```
Could not load the Qt platform plugin "xcb" in "/home//miniconda3/envs/test/lib/python3.8/site-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
Available platform plugins are: xcb, eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl.
```
it can be solved simply be installing opencv-headless
```
pip3 install opencv-python-headless
```
## Microsoft Visual C++ Build Tools Error When Installing MMDetection on Windows Machines 🪟
some windows machines may have this problem when installing **mmdet**
```
Building wheel for pycocotools (setup.py) ... error
...
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
```
You can try
```
conda install -c conda-forge pycocotools
```
or just use Visual Studio installer to Install `MSVC v143 - VS 2022 C++ x64/x86 build tools (Latest)**`
## Problem in installing mmcv-full
you may often stuck in installing `mmcv-full` with this message
```
Building wheels for collected packages: mmcv-full
Building wheel for mmcv-full (setup.py) ...
```
you can try installing [pytorch 1.13.1](https://pytorch.org/get-started/previous-versions/#v1131), instead of the lastest version, you can also refer to [this issue](https://github.com/open-mmlab/mmcv/issues/1386)
## Multiple copies of the OpenMP runtime have been linked into the program
you may encounter this problem
```
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program.
That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to
ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the
OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment
variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently
produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
```
you can solve this by upgrading numpy
```
pip install numpy==1.23.3
```
Thanks for [mohamedraafat96's issue](https://github.com/0ssamaak0/DLTA-AI/issues/52), you can check this [stackoverflow answer](https://stackoverflow.com/questions/64209238/error-15-initializing-libiomp5md-dll-but-found-libiomp5md-dll-already-initial) for more details
================================================
FILE: docs/index.md
================================================
---
label: "DLTA-AI User Guide"
icon: "assets/icon.png"
---
# DLTA-AI User Guide
Data Labeling, Tracking and Annotation with AI.
DLTA-AI is the next generation of annotation tools, integrating the power of Computer Vision SOTA models to Labelme in a seamless expirence and intuitive workflow to make creating image datasets easier than ever before
## Why DLTA-AI?
Open source and customizable annotation tool was created to fill a gap in annotation tools.
The customization and giving the user the full control was and will be our priority, from the model selection, input formats and inference parameters, to the export formats and even the User Interface itself. From these options, the goal was to extend the use cases of the concept of annotation tool to other use cases for end users beyond just preparing datasets to train models.
## Features
- Easy and straightforward Installation process, support for all Operating Systems
- User Guide with detailed tutorials for all the features
- Full Support of Auto Annotation with different models.
- Different annotation options and parameters (e.g., Thresholds)
- Export to (literally) any format
- Modern and functional User Interface
- Dedicated Video Mode
- Object Tracking Support
- Completely free and open-source, and will always be.
## Contributing
DLTA-AI is an open source project and contributions are very welcome
You can contribute in many ways:
- Create an [issue](https://github.com/0ssamaak0/DLTA-AI/issues) Reporting bugs 🐞 or suggesting new features 🌟 or just give your feedback 📝
- Create a [pull request](https://github.com/0ssamaak0/DLTA-AI/pulls) to fix bugs or add new features, or just to improve the code quality, optimize performance, documentation, or even just to fix typos
- Review [pull requests](https://github.com/0ssamaak0/DLTA-AI/pulls) and help with the code review process
- Spread the word about DLTA-AI and help us grow the community 🌎, by sharing the project on social media, or just by telling your friends about it
## Resources
- [Labelme](https://github.com/wkentaro/labelme)
- [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything)
- [MMDetection](https://github.com/open-mmlab/mmdetection/tree/2.x)
- [ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
- [mikelbrostrom yolov8_tracking](https://github.com/mikel-brostrom/yolov8_tracking)
- [orjson](https://github.com/ijl/orjson)
- [icons8](https://icons8.com/)
================================================
FILE: docs/main_features/Export.md
================================================
---
icon: ":outbox_tray:"
order: 3
---
# Export
This page is under construction 🚧, please check back later.
================================================
FILE: docs/main_features/SAM.md
================================================
---
icon: https://github.com/0ssamaak0/DLTA-AI/blob/master/DLTA_AI_app/labelme/icons/SAM.png?raw=true
order: 10
---
# Segment Anything (SAM)
META AI model [Segment Anything](https://segment-anything.com/) or `SAM` is integrated in DLTA-AI in many ways to increase the accuracy of the Annotation process, in a very native user expirience with almost zero effort to install.
## Installation
Like all other models, The [Model Explorer]("../model_selection/model_explorer.md") can be used to install the checkpoints directly with just a single click
[!embed](https://youtu.be/8g15M9bE1uA?t=5)
## Segmentation
Segment Anything can be used to make Zero-Shot Segmentation of any object.
DLTA-AI provides an expiernce similar to the [Original Demo](https://segment-anything.com/demo#) with the simple SAM toolbar that supports user-customized shortcuts, and runs locally on the user machine on any image or video.
[!embed](https://youtu.be/8g15M9bE1uA?t=41)
## Enhance Polygons
Beside the usual functionality of Zero-Shot Segmentation, Segment Anything can be used to enhance the accuracy of any polygon, weather it was created by the user or by any other model, by simply selecting the polygon(s) and enhancing them from the toolbar or the context menu.
[!embed](https://youtu.be/8g15M9bE1uA?t=84)
## Interpolation Tracking
DLTA-AI utilizes the power of Segment Anything to provide a very accurate interpolation tracking, that can be used to track any object in a video, and can be used to track multiple objects at the same time.
[!embed](https://youtu.be/8g15M9bE1uA?t=116)
================================================
FILE: docs/main_features/index.yml
================================================
label: Main Features
icon: ":star2:"
expanded: true
order: 40
================================================
FILE: docs/main_features/inputs.md
================================================
---
icon: ":clapper:"
order: 8
---
# Input Modes
DLTA-AI provides different options for inputs
## Image Mode
image mode is very simple, just open an imgae and start annotating
[!embed](https://youtu.be/zkm_GhX8OtM?t=6)
## Directory Mode
Directory mode is used to annotate a directory of images, it's very useful when you have a dataset of images and you want to annotate them all at once.
Note that it shows all images within the directory and all subdirectories.
[!embed](https://youtu.be/zkm_GhX8OtM?t=17)
## Video Mode
Video mode is used to annotate a video, and provides an integrated video player that allows you to naivgate, play, pause, forward, backward, and jump to a specific frame.
[!embed](https://youtu.be/zkm_GhX8OtM?t=41)
## Video as Frames
you can open a video as a directory of frames, this is useful when you want to just annotate some frames of a video. you have the option to set start and end frame, and also the sampling rate i.e., the step between frames.
[!embed](https://youtu.be/zkm_GhX8OtM?t=71)
================================================
FILE: docs/main_features/segmentation.md
================================================
---
icon: ":art:"
order: 9
---
# Segmentation
Instance Segmentation is one of the major features in DLTA-AI, from the huge library to the different options and pramaters, to the ability to apply manual edits to the results, DLTA-AI proivdes a fully customizable and easy to use segmentation experience.
## Model Selection
The model Selection can be done directly by selecting a segmentation model from the menu, or by selecting a model from the huge library of models in the [Model Explorer](../model_selection/model_explorer.md)
[!embed](https://youtu.be/bYjy82Ug2wU?t=10)
## Inferencing
The model can be run on the current image only (works in all [input modes](inputs.md)) or on all images (directory mode only)
[!embed](https://youtu.be/bYjy82Ug2wU?t=28)
## Visualization Options
you can select the visualization options from the menu, such as showing the segmentation mask, or just bounding box, and the class name and the cofidence scor as well
[!embed](https://youtu.be/bYjy82Ug2wU?t=60)
## Select Classes
you can select some classes among the 80 of [COCO classes](https://cocodataset.org/) you can select for just this use (forgotten when you close DLTA-AI) or set them as default classes (saved when you close DLTA-AI)
[!embed](https://youtu.be/bYjy82Ug2wU?t=84)
## Thresholds
To give the annotator the full control and the ability to choose the optimum point in the precision/recall tradeoff, DLTA-AI provides 2 thresholding options
### Confidence Threshold
Confidence threshold is very simple, by just typing the threshold value of setting it through the slider, all predictions with confidence less than the threshold will be ignored
[!embed](https://youtu.be/bYjy82Ug2wU?t=112)
### IOU Threshold (For Non Maximum Suppression)
DLTA-AI internally applies Non Maximum Suppression (NMS) to the predictions, to remove the overlapping predictions, the IOU threshold is the threshold used in NMS.
[!embed](https://youtu.be/bYjy82Ug2wU?t=150)
================================================
FILE: docs/main_features/tracking/index.yml
================================================
label: Tracking
icon: https://github.com/0ssamaak0/DLTA-AI/blob/master/DLTA_AI_app/labelme/icons/tracking.png?raw=true
expanded: true
order: 4
================================================
FILE: docs/main_features/tracking/interpolation.md
================================================
---
icon:
order: 1
---
# Interpolation Tracking
## Interpolation Method
### Linear Interpolation
### SAM Interpolation
## Interpolation Between
### Selected Keyframes
### Detected Frames
================================================
FILE: docs/main_features/tracking/tracking.md
================================================
---
icon:
order: 2
---
# Tracking
## Model Selection
## Tracking Options
## Visualization Options
## Edit propagation
## Delete Options
================================================
FILE: docs/model_selection/index.yml
================================================
label: Model Selection
icon: ":robot_face:"
expanded: true
order: 30
================================================
FILE: docs/model_selection/merge.md
================================================
---
icon: https://github.com/0ssamaak0/DLTA-AI/blob/master/DLTA_AI_app/labelme/icons/merge.png?raw=true
order: 5
---
# Merge Models
This page is under construction 🚧, please check back later.
================================================
FILE: docs/model_selection/model_explorer.md
================================================
---
icon: https://github.com/0ssamaak0/DLTA-AI/blob/master/DLTA_AI_app/labelme/icons/checklist.png?raw=true
order: 10
---
# Model Explorer
This page is under construction 🚧, please check back later.
================================================
FILE: docs/retype.yml
================================================
input: .
output: .retype
url: # Add your website address here
branding:
title: DLTA-AI User Guide
logo: assets/icon.png
colors:
label:
text: "#ffffff"
background: "#ff0000"
favicon: assets/icon.png
links:
- text: GitHub
link: https://github.com/0ssamaak0/DLTA-AI
icon: mark-github
- text: Issues
link: https://github.com/0ssamaak0/DLTA-AI/issues
icon: bug
- text: Release Notes
link: https://github.com/0ssamaak0/DLTA-AI/releases
icon: note
- text: Youtube Channel
link: https://www.youtube.com/@DLTA-AI
icon: video
footer:
copyright: "DLTA-AI is licensed under GPLv3 License"
links:
- text: License
link: https://github.com/0ssamaak0/DLTA-AI/blob/master/LICENSE
icon: law
================================================
FILE: docs/user_interface.md
================================================
---
label: User Interface
icon: https://github.com/0ssamaak0/DLTA-AI/blob/master/DLTA_AI_app/labelme/icons/UI.png?raw=true
order: 1
---
This page is under construction 🚧, please check back later.
================================================
FILE: releasenotes.md
================================================
# New Features 🌟
-
# Bug Fixes 🐞
- Closing the save dialog without saving annotations no more enables the export button #45
================================================
FILE: requirements.txt
================================================
PyQt6==6.6.0
QtPy==2.3.1
termcolor==2.2.0
imgviz==1.7.2
opencv-python==4.7.0.72
pyqtdarktheme==2.1.0
supervision==0.3.2
gdown==4.7.1
ultralytics==8.0.61
onemetric==0.1.2
easydict==1.10
openmim==0.3.2
mmdet==2.25.2
scikit-image==0.20.0
filterpy==1.4.5
segment-anything==1.0
lap==0.4.0
orjson==3.8.12
notify-py==0.3.42
psutil==5.9.4
shapely==2.0.1
screeninfo==0.8.1
================================================
FILE: setup.py
================================================
import setuptools
with open("README.md", "r", encoding="utf8") as fh:
long_description = fh.read()
with open("requirements.txt", "r") as f:
requirements = f.read().splitlines()
__version__ = "0.0.0"
with open("DLTA_AI_app/labelme/__init__.py", "r") as f:
for line in f.readlines():
if line.startswith("__version__"):
__version__ = line.split("=")[1].strip().strip('"')
break
setuptools.setup(
name="DLTA-AI",
version=f"{__version__}",
author="0ssamaak0",
author_email="0ssamaak0@gmail.com",
description="DLTA-AI is the next generation of annotation tools, integrating the power of Computer Vision SOTA models to Labelme in a seamless expirence and intuitive workflow to make creating image datasets easier than ever before",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/0ssamaak0/DLTA-AI",
package_dir={"DLTA_AI_app": "DLTA_AI_app"},
python_requires='>=3.8',
install_requires=requirements,
package_data={"": ["*"]},
license="GPLv3",
entry_points={
"console_scripts": [
"DLTA-AI=DLTA_AI_app.__main__:main"
]
}
)
================================================
FILE: yolo training commands.txt
================================================
yolo task=segment mode=train model=yolov8n-seg.pt epochs= 1 imgsz = 320 workers =2 batch = 4 data=datasets\thermalseg.v6i.yolov8\data.yaml