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YOLOv8 Retraining

  • To learn more about yolov8 look here

Prerequisites

NOTE: In case you are using the Hailo Software Suite docker, make sure to run all of the following instructions outside of that docker.

Environment Preparations

  1. Build the docker image:
    cd hailo_model_zoo/training/yolov8
docker build --build-arg timezone=`cat /etc/timezone` -t yolov8:v0 .
    the following optional arguments can be passed via --build-arg:
    • timezone - a string for setting up timezone. E.g. "Asia/Jerusalem"
    • user - username for a local non-root user. Defaults to 'hailo'.
    • group - default group for a local non-root user. Defaults to 'hailo'.
    • uid - user id for a local non-root user.
    • gid - group id for a local non-root user.
    * This command will build the docker image with the necessary requirements using the Dockerfile exists in yolov8 directory.
  2. Start your docker:
    docker run --name "your_docker_name" -it --gpus all --ipc=host -v  /path/to/local/data/dir:/path/to/docker/data/dir yolov8:v0
    • docker run create a new docker container.
    • --name <your_docker_name> name for your container.
    • -it runs the command interactively.
    • --gpus all allows access to all GPUs.
    • --ipc=host sets the IPC mode for the container.
    • -v /path/to/local/data/dir:/path/to/docker/data/dir maps /path/to/local/data/dir from the host to the container. You can use this command multiple times to mount multiple directories.
    • yolov8:v0 the name of the docker image.

Training and exporting to ONNX

  1. Train your model:
    Once the docker is started, you can start training your model.
    • Prepare your custom dataset - Follow the steps described here in order to create:
      • dataset.yaml configuration file
      • Labels - each image should have labels in YOLO format with corresponding txt file for each image.
      • Make sure to include number of classes field in the yaml, for example: nc: 80
    • Start training - The following command is an example for training a yolov8s model.
      yolo detect train data=coco128.yaml model=yolov8s.pt name=retrain_yolov8s epochs=100 batch=16
      • yolov8s.pt - pretrained weights. The pretrained weights for yolov8n, yolov8s, yolov8m, yolov8l and yolov8x will be downloaded to your working directory when running this command.
      • coco128.yaml - example file for data.yaml file. Can be found at ultralytics/ultralytics/datasets.
      • retrain_yolov8s - the new weights will be saved at ultralytics/runs/detect/retrain_yolov8s.
      • epochs - number of epochs to run. default to 100.
      • batch - number of images per batch. default to 16.

    NOTE: more configurable parameters can be found at https://docs.ultralytics.com/modes/train/

  2. Export to ONNX:
    In order to export your trained YOLOv8 model to ONNX run the following script:
    yolo export model=/path/to/trained/best.pt imgsz=640 format=onnx opset=11  # export at 640x640

    NOTE: more configurable parameters can be found at https://docs.ultralytics.com/modes/export/

Compile the Model using Hailo Model Zoo

You can generate an HEF file for inference on Hailo-8 from your trained ONNX model.
In order to do so you need a working model-zoo environment.
Choose the corresponding YAML from our networks configuration directory, i.e. hailo_model_zoo/cfg/networks/yolov8s.yaml, and run compilation using the model zoo:
hailomz compile --ckpt yolov8s.onnx --calib-path /path/to/calibration/imgs/dir/ --yaml path/to/yolov8s.yaml --start-node-names name1 name2 --end-node-names name1 --classes 80
  • --ckpt - path to your ONNX file.
  • --calib-path - path to a directory with your calibration images in JPEG/png format
  • --yaml - path to your configuration YAML file.
  • --start-node-names and --end-node-names - node names for customizing parsing behavior (optional).
  • --classes - adjusting the number of classes in post-processing configuration (optional).
  • The model zoo will take care of adding the input normalization to be part of the model.

Note

  • Make sure to also update preprocessing.input_shape field on yolo.yaml, if it was changed on retraining.

More details about YAML files are presented here.