YOLOv8 ML backend for the Label Studio

This project contains an ML backend for classifying pills in Label Studio. It uses the YOLOv8 model and can segment and classify pills as capsules or tablets. This project is a fork of label-studio-yolov8-backend written by seblful.

Project Structure

The repository contains the following files and directories:

• Dockerfile: The Dockerfile for building the backend container.

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

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

• models/: The pre-trained YOLOv8 models for classification. best-detect.pt was trained on video frames from the entrance of the AppMAIS hives to detect and classify drone and worker honeybees. stripe-seg.pt was trained on segmented bee images to segment the stripes from the bee abdomens.

• config_files/: The config files associated with each model. config-detect.yaml specifies the required attributes for the best-detect.pt trained model and config-seg.yaml does the same for stripe-seg.pt. The required attributes are:

  • MODEL_PATH: Path to the model, such as "./models/best-detect.pt".
  • MODEL_TYPE: Model output type, currently only supports "segment" or "detect", with polygon labels and rectangle labels respectively.
  • MODEL_NAME: Any name for the model, only used to concatenate with the MDOEL_VERSION to record what model gave the annotation. Annotation information recorded as <MODEL_NAME>-<MDOEL_VERSION>.
  • MODEL_VERSION: Any string for the model version, only used to concatenate with the MDOEL_NAME to record what model gave the annotation. Annotation information recorded as <MODEL_NAME>-<MDOEL_VERSION>.
  • MODEL_CLASSES: List of strings for the names of the model classes. Case-sensitivity applies here for matching model classes to the label classes in Label Studio.

• uwsgi.ini: The uWSGI configuration file for running the backend.

• supervisord.conf: The supervisord configuration file for running the backend processes.

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

• credentials.yaml: Not saved in the repo, but required to run! this file is similar to the config.yaml files, with only two fields. The fields are LS_URL written as "http://<host>:<port>", and LS_API_TOKEN written as "<API_KEY>".

Getting Started

1. Clone the Label Studio Machine Learning Backend git repository.

2. Import or create a new credentials.yaml file

3. Navigate into the label-studio-yolov8-backend directory if you have not already.

4. Run the command:

   label-studio-ml start . -p <port> --kwarg config_path=<path_to_config_file> credentials_path=<path_to_credentials_file>

   The . refers to starting the yolov8 backend in the current directory. The two kwargs allow you to specify a destination for your configuration and credentials yaml files, and default to ./config.yaml and ./credentials.yaml. The port defaults to 9090. This will start the backend.

   Check if the backend is running (on port 9090 for example):

   $ curl http://localhost:9090/health

   {"status":"UP"}

5. Connect running backend to Label Studio:

   label-studio start --init new_project --ml-backends http://localhost:9090

6. Start the labeling process.

Running multiple backends at a time

This backend supports running multiple backends at a time through the keyword arguments. For example you could run:

label-studio-ml start . -p 9090 --kwarg config_path=./config_files/config-seg.yaml credentials_path=./credentials.yaml

And

label-studio-ml start . -p 9091 --kwarg config_path=./config_files/config-detect.yaml credentials_path=./credentials.yaml

And you would have the segmentation model on port 9090 and the detect model on port 9091. The only value that must change is the port, as port 9090 would be busy after running the first command, but you most likely need to change the config path as well if you would like to have a different model running.

Training

Model training is not included in this project. This may be added later.