Anylabeling

<p align="center"> <img alt="AnyLabeling" style="width: 128px; height: 128px; height: auto;" src="https://github.com/user-attachments/assets/847e47e6-acf0-4f96-9ed9-5485ab405ae0"/> <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>

<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
• Documentation: https://anylabeling.nrl.ai

Features:

• [x] Image annotation for polygon, rectangle, circle, line and point.
• [x] Auto-labeling with YOLOv8 (object detection).
• [x] Auto-labeling with Segment Anything family:
  • SAM (ViT-B / ViT-L / ViT-H) and MobileSAM
  • SAM 2 and SAM 2.1 (Hiera-Tiny / Small / Base+ / Large)
  • SAM 3 (ViT-H) — open-vocabulary segmentation with text prompts
• [x] Text detection, recognition and KIE (Key Information Extraction) labeling.
• [x] Multiple languages availables: English, Vietnamese, Chinese.

Supported Models

| Model | Prompt Types | Notes | |-------|-------------|-------| | SAM ViT-B / ViT-L / ViT-H | Point, Rectangle | Original Segment Anything | | MobileSAM | Point, Rectangle | Lightweight SAM | | SAM 2 Hiera-Tiny / Small / Base+ / Large | Point, Rectangle | Meta SAM 2 | | SAM 2.1 Hiera-Tiny / Small / Base+ / Large | Point, Rectangle | Improved SAM 2 | | SAM 3 ViT-H | Text, Point, Rectangle | Open-vocabulary; text drives detection | | YOLOv8n / s / m / l / x | — | Object detection & auto-labeling |

All models are downloaded automatically on first use from Hugging Face.

Install and Run

1. Download and run executable

• Download and run newest version from Releases.
• For MacOS:
  • Download the folder mode build (AnyLabeling-Folder.zip) from Releases
  • See macOS folder mode instructions for details

Install from Pypi

• Requirements: Python 3.10+. Recommended: Python 3.12.

• Recommended: Miniconda/Anaconda.

• Create environment:

conda create -n anylabeling python=3.12
conda activate anylabeling

• (For macOS only) Install PyQt6 using Conda:

conda install -c conda-forge pyqt=6

• Install anylabeling:

pip install anylabeling # or pip install anylabeling-gpu for GPU support

• Start labeling:

anylabeling

Documentation

Website: https://anylabeling.nrl.ai/

Applications

| Object Detection | Recognition | Facial Landmark Detection | 2D Pose Estimation | | :---: | :---: | :---: | :---: | | <img src='https://user-images.githubusercontent.com/72010077/273488633-fc31da5c-dfdd-434e-b5d0-874892807d95.png' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/72010077/277396071-79daec2c-6b0a-4d42-97cf-69fd098b3400.png' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/61035602/206095684-72f42233-c9c7-4bd8-9195-e34859bd08bf.jpg' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/61035602/206100220-ab01d347-9ff9-4f17-9718-290ec14d4205.gif' height="126px" width="180px"> | | 2D Lane Detection | OCR | Medical Imaging | Instance Segmentation | | <img src='https://user-images.githubusercontent.com/72010077/273764641-65f456ed-27ce-4077-8fce-b30db093b988.jpg' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/72010077/273421210-30d20e08-3b72-4f4d-8976-05b564e13d87.png' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/72010077/273764318-e8b6a197-e733-478e-a210-e4386bafa1e4.png' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/61035602/206095831-cc439557-1a23-4a99-b6b0-b6f2e97e8c57.jpg' height="126px" width="180px"> | | Image Tagging | Rotation | And more! | | <img src='https://user-images.githubusercontent.com/72010077/277670825-8797ac7e-e593-45ea-be6a-65c3af17b12b.png' height="126px" width="180px"> | <img src='https://user-images.githubusercontent.com/72010077/277395955-aab54ea0-88f5-41af-ab0a-f4158a673f5e.png' height="126px" width="180px"> | Your applications here! |

Development

• Install packages:

pip install -r requirements-dev.txt
# or pip install -r requirements-macos-dev.txt for MacOS

• Generate resources:

pyrcc5 -o anylabeling/resources/resources.py anylabeling/resources/resources.qrc

• Run app:

python anylabeling/app.py

Build executable

• Install PyInstaller:

pip install -r requirements-dev.txt

• Build:

bash build_executable.sh

• Check the outputs in: dist/.

Contribution

If you want to contribute to AnyLabeling, please read Contribution Guidelines.

Star history

References

• Labeling UI built with ideas and components from LabelImg, LabelMe.
• Auto-labeling with Segment Anything (SAM, SAM 2, SAM 2.1, SAM 3), MobileSAM.
• Auto-labeling with YOLOv8.
• Icons from FlatIcon: DinosoftLabs, Freepik, Vectoricons, HideMaru.