Integrate custom YOLOv8 model into CVAT for automatic annotation.

screenshot

Installation (Linux Ubuntu)

In the CVAT directory, run:

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

Create an account

docker exec -it cvat_server bash -ic 'python3 ~/manage.py createsuperuser'

Install nuctl*

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

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

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/
```
Troubleshooting
- Check that your nuclio function is running correctly
  docker ps --filter NAME=custom-model-yolov8:
```
CONTAINER ID   IMAGE                        COMMAND       CREATED          STATUS                    PORTS                                         NAMES
3dc54494bbb8   custom-model-yolov8:latest   "processor"   52 minutes ago   Up 52 minutes (healthy)   0.0.0.0:32896->8080/tcp, :::32896->8080/tcp   nuclio-nuclio-custom-model-yolov8
```
- If it is not running check the container's log, it might reveal what is going wrong:
  docker logs 3dc54494bbb8 (CONTAINER ID)
```
...
24.01.31 12:28:35.522                 processor (D) Processor started
24.01.31 12:29:27.171 sor.http.w0.python.logger (I) Run custom-model-yolov8 model {"worker_id": "0"}
```
- If installing on WSL2, you should update the ".wslconfig" file WLS2 to allow WSL at least 6 GB RAM. Put the .wslconfig file in the C:\/Users\/\<username> folder. If DOCKER crashes frequently or auto annotation fails immediately after starting, it is probably because of low RAM. Use the alternative function if the installation fails (worked for CVAT-2.16.2 installation in WSL2, with 6 GB allowed to WSL2).

Note: * is a one time step.

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.