its-jd / custom-yolov8-auto-annotation-cvat-blueprint

Integrate custom YOLOv8 model into CVAT for automatic annotation blueprint.

Integrate custom YOLOv8 model into CVAT for automatic annotation.

Installation (Linux Ubuntu)

Generally would follow this documentation (https://opencv.github.io/cvat/docs/administration/advanced/installation_automatic_annotation/)

In the CVAT directory, run:

1. Stop all containers first, if any.

   docker compose down
   

2. Start CVAT together with the plugin use for AI automatic annotation assistant.

   docker compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up -d
   

3. Install nuctl*

   wget https://github.com/nuclio/nuclio/releases/download/<version>/nuctl-<version>-linux-amd64
   

4. After downloading the nuclio, give it a proper permission and do a softlink.*

   sudo chmod +x nuctl-<version>-linux-amd64
   sudo ln -sf $(pwd)/nuctl-<version>-linux-amd64 /usr/local/bin/nuctl
   

5. Build the docker image and run the container. After it is done, you can use the model right away in the CVAT.

   ./serverless/deploy_cpu.sh path/to/this/folder/
   

Note: * is a one time step.

File Structure

• function.yaml: Declare the model so it can be understand by CVAT. It includes setup the docker environment.

• main.py: Contain the handle function that will serve as the endpoint used by CVAT to run detection.

• custom-yolov8n.pt: Your custom yolov8 model.

References

1. https://opencv.github.io/cvat/docs/manual/advanced/serverless-tutorial/#adding-your-own-dl-models

   Official documentation on how to add the custom model.

2. https://stephencowchau.medium.com/journey-using-cvat-semi-automatic-annotation-with-a-partially-trained-model-to-tag-additional-8057c76bcee2