Custom YOLO Backend for Label Studio

This backend provides ML-assisted labeling capabilities to accelerate your annotation workflow, supporting both object detection and instance segmentation tasks.

🏗️ Project Structure

• Dockerfile: The Dockerfile for building the backend container.

• docker-compose.yml: The docker-compose file for running the backend.

• _wsgi.py: WSGI app initializer.

• start.sh: bash script to start the whole process.

• model.py: The Python code for the ML backend model.

• requirements.txt: The list of Python dependencies for the backend.

🚀 Quick Start

1. Clone the repository:

   git clone https://github.com/seblful/label-studio-yolo-backend.git
   cd label-studio-yolo-backend

2. Create and prepare your model directory:

   mkdir models
   cp /path/to/your/model.pt models/

3. Edit .env with your settings:

   BASIC_AUTH_USER=  # Optional
   BASIC_AUTH_PASS=  # Optional
   LOG_LEVEL=DEBUG
   
   MODEL_FILENAME=model.pt
   
   PORT=8080
   
   LABEL_STUDIO_API_KEY= # API key from LS
   TASK_TYPE=segmentation # segmentation or detection

4. Deploy using the following command:

   docker compose up

5. Add the model in project settings:

   From the project settings, select the Model page and click Connect Model.

   Add the URL http://locallhost:9090 and save the model as an ML backend.

   

6. Label in interactive mode

   To use this functionality, activate Auto-Annotation.

For users with internet restrictions:

Configure Docker daemon with proxy:

{
  "registry-mirrors": ["https://registry.docker-cn.com"]
}

📋 TODO

• Add support for obb and keypoints.

💁 Contributing

Contributions to this project are welcome. To contribute, please submit an issue or pull request.