Label Studio 工具的 ML 后端 后端使用 YOLOv8 模型进行图像分割或对象检测 .zip

# YOLOv8 ML backend for the Label Studio YOLOv8 interactive ML-assisted labeling, facilitating faster annotation for **image detection**, ***instance* image segmentation**. Tested against Label Studio 1.13.1. ## 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. ## Setup process Before you begin: * Ensure git is installed * Ensure Docker Compose is installed. ### 1. Install Label Studio Launch Label Studio. You can follow the guide from the [official documentation](https://labelstud.io/guide/install.html) or use the following commands: If you're using local file serving, be sure to [get a copy of the API token](https://labelstud.io/guide/user_account#Access-token) from Label Studio to connect the model. ### 2. Create a Label Studio project Create a new project. In the project **Settings** set up the **Labeling Interface** for **image detection** (RectangleLabels) or **image segmentation** (PolygonLabels). ### 3. Install label-studio-yolov8-backend Download the Label Studio YOLOv8 backend repository. ``` git clone https://github.com/seblful/label-studio-yolov8-backend.git cd label-studio-yolov8-backend ``` Configure parameters in `.env` file: ``` LABEL_STUDIO_URL=<IPv4 Address> (check your ipconfig) LABEL_STUDIO_API_KEY=<Label Studio API token> TASK_TYPE=<segmentation> or <detection> ``` ### 4. Start the servers ``` docker compose up ``` ### 5. Upload tasks Upload images directly to Label Studio using the Label Studio interface. ### 6. Add model in project settings From the project settings, select the **Model** page and click [**Connect Model**](https://labelstud.io/guide/ml#Connect-the-model-to-Label-Studio). Add the URL `http://locallhost:9090` and save the model as an ML backend. ### 7. Label in interactive mode To use this functionality, activate **Auto-Annotation** for drawing boxes. ## Training Model training is **not included** in this project. This will probably be added later. ## Contributing Contributions to this project are welcome. To contribute, please submit an issue or pull request.