<p align="center">
<img alt="AnyLabeling" style="width: 128px; max-width: 100%; height: auto;" src="https://user-images.githubusercontent.com/18329471/232250539-2b15b9ee-5593-41d0-ba22-e0442f314cce.png"/>
<h1 align="center">🌟 AnyLabeling 🌟</h1>
<p align="center">Effortless data labeling with AI support from <b>YOLO</b> and <b>Segment Anything</b>!</p>
<p align="center"><b>AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling</b></p>
</p>
![](https://user-images.githubusercontent.com/18329471/234640541-a6a65fbc-d7a5-4ec3-9b65-55305b01a7aa.png)
[![PyPI](https://img.shields.io/pypi/v/anylabeling)](https://pypi.org/project/anylabeling)
[![license](https://img.shields.io/github/license/vietanhdev/anylabeling.svg)](https://github.com/vietanhdev/anylabeling/blob/master/LICENSE)
[![open issues](https://isitmaintained.com/badge/open/vietanhdev/anylabeling.svg)](https://github.com/vietanhdev/anylabeling/issues)
[![Pypi Downloads](https://pepy.tech/badge/anylabeling)](https://pypi.org/project/anylabeling/)
[![Documentation](https://img.shields.io/badge/Read-Documentation-green)](https://anylabeling.nrl.ai/)
<a href="https://youtu.be/5qVJiYNX5Kk">
<img alt="AnyLabeling" src="https://raw.githubusercontent.com/vietanhdev/anylabeling/master/assets/screenshot.png"/>
</a>
**Auto Labeling with Segment Anything**
<a href="https://youtu.be/5qVJiYNX5Kk">
<img style="width: 800px; margin-left: auto; margin-right: auto; display: block;" alt="AnyLabeling-SegmentAnything" src="https://user-images.githubusercontent.com/18329471/236625792-07f01838-3f69-48b0-a12e-30bad27bd921.gif"/>
</a>
- **Youtube Demo:** [https://www.youtube.com/watch?v=5qVJiYNX5Kk](https://www.youtube.com/watch?v=5qVJiYNX5Kk)
- **Documentation:** [https://anylabeling.nrl.ai](https://anylabeling.nrl.ai)
**Features:**
- [x] Image annotation for polygon, rectangle, circle, line and point.
- [x] Auto-labeling with YOLOv5 and Segment Anything.
- [x] Text detection, recognition and KIE (Key Information Extraction) labeling.
- [x] Multiple languages availables: English, Vietnamese, Chinese.
## I. Install and run
### 1. Download and run executable
- Download and run newest version from [Releases](https://github.com/vietanhdev/anylabeling/releases).
- For MacOS:
- After installing, go to Applications folder
- Right click on the app and select Open
- From the second time, you can open the app normally using Launchpad
### 2. Install from Pypi
- Requirements: Python >= 3.8, <= 3.10.
- Recommended: [Miniconda/Anaconda](https://docs.conda.io/en/latest/miniconda.html).
- Create environment:
```bash
conda create -n anylabeling python=3.8
conda activate anylabeling
```
- **(For macOS only)** Install PyQt5 using Conda:
```bash
conda install -c conda-forge pyqt==5.15.7
```
- Install anylabeling:
```bash
pip install anylabeling # or pip install anylabeling-gpu for GPU support
```
- Start labeling:
```bash
anylabeling
```
## II. Development
- Generate resources:
```bash
pyrcc5 -o anylabeling/resources/resources.py anylabeling/resources/resources.qrc
```
- Run app:
```bash
python anylabeling/app.py
```
## III. Build executable
- Install PyInstaller:
```bash
pip install -r requirements-dev.txt
```
- Build:
```bash
bash build_executable.sh
```
- Check the outputs in: `dist/`.
## IV. Contribution
If you want to contribute to **AnyLabeling**, please read [Contribution Guidelines](https://anylabeling.nrl.ai/docs/contribution).
## V. Star history
[![Star History Chart](https://api.star-history.com/svg?repos=vietanhdev/anylabeling&type=Date)](https://star-history.com/#vietanhdev/anylabeling&Date)
## VI. References
- Labeling UI built with ideas and components from [LabelImg](https://github.com/heartexlabs/labelImg), [LabelMe](https://github.com/wkentaro/labelme).
- Auto-labeling with [Segment Anything Models](https://segment-anything.com/), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM).
- Auto-labeling with [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics).
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"description": "<p align=\"center\">\n <img alt=\"AnyLabeling\" style=\"width: 128px; max-width: 100%; height: auto;\" src=\"https://user-images.githubusercontent.com/18329471/232250539-2b15b9ee-5593-41d0-ba22-e0442f314cce.png\"/>\n <h1 align=\"center\">\ud83c\udf1f AnyLabeling \ud83c\udf1f</h1>\n <p align=\"center\">Effortless data labeling with AI support from <b>YOLO</b> and <b>Segment Anything</b>!</p>\n <p align=\"center\"><b>AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling</b></p>\n</p>\n\n![](https://user-images.githubusercontent.com/18329471/234640541-a6a65fbc-d7a5-4ec3-9b65-55305b01a7aa.png)\n\n[![PyPI](https://img.shields.io/pypi/v/anylabeling)](https://pypi.org/project/anylabeling)\n[![license](https://img.shields.io/github/license/vietanhdev/anylabeling.svg)](https://github.com/vietanhdev/anylabeling/blob/master/LICENSE)\n[![open issues](https://isitmaintained.com/badge/open/vietanhdev/anylabeling.svg)](https://github.com/vietanhdev/anylabeling/issues)\n[![Pypi Downloads](https://pepy.tech/badge/anylabeling)](https://pypi.org/project/anylabeling/)\n[![Documentation](https://img.shields.io/badge/Read-Documentation-green)](https://anylabeling.nrl.ai/)\n\n<a href=\"https://youtu.be/5qVJiYNX5Kk\">\n <img alt=\"AnyLabeling\" src=\"https://raw.githubusercontent.com/vietanhdev/anylabeling/master/assets/screenshot.png\"/>\n</a>\n\n**Auto Labeling with Segment Anything**\n\n<a href=\"https://youtu.be/5qVJiYNX5Kk\">\n <img style=\"width: 800px; margin-left: auto; margin-right: auto; display: block;\" alt=\"AnyLabeling-SegmentAnything\" src=\"https://user-images.githubusercontent.com/18329471/236625792-07f01838-3f69-48b0-a12e-30bad27bd921.gif\"/>\n</a>\n\n\n- **Youtube Demo:** [https://www.youtube.com/watch?v=5qVJiYNX5Kk](https://www.youtube.com/watch?v=5qVJiYNX5Kk)\n- **Documentation:** [https://anylabeling.nrl.ai](https://anylabeling.nrl.ai)\n\n**Features:**\n\n- [x] Image annotation for polygon, rectangle, circle, line and point.\n- [x] Auto-labeling with YOLOv5 and Segment Anything.\n- [x] Text detection, recognition and KIE (Key Information Extraction) labeling.\n- [x] Multiple languages availables: English, Vietnamese, Chinese.\n\n## I. Install and run\n\n### 1. Download and run executable\n\n- Download and run newest version from [Releases](https://github.com/vietanhdev/anylabeling/releases).\n- For MacOS:\n - After installing, go to Applications folder\n - Right click on the app and select Open\n - From the second time, you can open the app normally using Launchpad\n\n### 2. Install from Pypi\n\n- Requirements: Python >= 3.8, <= 3.10.\n- Recommended: [Miniconda/Anaconda](https://docs.conda.io/en/latest/miniconda.html).\n\n- Create environment:\n\n```bash\nconda create -n anylabeling python=3.8\nconda activate anylabeling\n```\n\n- **(For macOS only)** Install PyQt5 using Conda:\n\n```bash\nconda install -c conda-forge pyqt==5.15.7\n```\n\n- Install anylabeling:\n\n```bash\npip install anylabeling # or pip install anylabeling-gpu for GPU support\n```\n\n- Start labeling:\n\n```bash\nanylabeling\n```\n\n## II. Development\n\n- Generate resources:\n\n```bash\npyrcc5 -o anylabeling/resources/resources.py anylabeling/resources/resources.qrc\n```\n\n- Run app:\n\n```bash\npython anylabeling/app.py\n```\n\n## III. Build executable\n\n- Install PyInstaller:\n\n```bash\npip install -r requirements-dev.txt\n```\n\n- Build:\n\n```bash\nbash build_executable.sh\n```\n\n- Check the outputs in: `dist/`.\n\n## IV. Contribution\n\nIf you want to contribute to **AnyLabeling**, please read [Contribution Guidelines](https://anylabeling.nrl.ai/docs/contribution).\n\n## V. Star history\n\n[![Star History Chart](https://api.star-history.com/svg?repos=vietanhdev/anylabeling&type=Date)](https://star-history.com/#vietanhdev/anylabeling&Date)\n\n## VI. References\n\n- Labeling UI built with ideas and components from [LabelImg](https://github.com/heartexlabs/labelImg), [LabelMe](https://github.com/wkentaro/labelme).\n- Auto-labeling with [Segment Anything Models](https://segment-anything.com/), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM).\n- Auto-labeling with [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics).\n",
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