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YOLOv3 Retraining

  • To learn more about yolov3 look here

Prerequisites

NOTE:In case you use Hailo Software Suite docker, make sure you are doing all the following instructions outside of this docker.

Environment Preparations

  1. Build the docker image:
    cd hailo_model_zoo/training/yolov3
docker build --build-arg timezone=`cat /etc/timezone` -t yolov3: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 this 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 yolov3: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.
    • yolov3: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 YOLOv3.

    • Prepare your custom dataset - Follow the full instructions described here:

      • Create data/obj.data with paths to your training and validation .txt files, which contain the list of the image paths*.
        classes = 80
train  = data/train.txt
valid  = data/val.txt
names = data/coco.names
backup = backup/
        * Tip: specify the paths to the training and validation images in the training and validation .txt files relative to /workspace/darknet/
        Place your training/validation images and labels in your data folder and make sure you update the number of classes.

      • Labels - each image should have labels in YOLO format with corresponding txt file for each image.

    • Start training - The following command is an example for training the yolov3.
    ./darknet detector train data/obj.data cfg/yolov3.cfg yolov3.weights -map -clear
    Final trained weights will be available in backup/ directory.
  2. Export to ONNX:
    In order to export your trained YOLOv3 model to ONNX run the following script:
    python ../pytorch-YOLOv4/demo_darknet2onnx.py cfg/yolov3.cfg /path/to/trained.weights /path/to/some/image.jpg 1
    • The ONNX would be available in /workspace/darknet/

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/yolov3_416.yaml (for the default YOLOv3 model).

Align the corresponding alls, i.e. hailo_model_zoo/cfg/alls/base/yolov3_416.alls with the size of the calibration set using dataset_size=<number_of_jpgs_in_folder> parameter.

Run compilation using the model zoo:
hailomz compile  yolov3_416 --ckpt yolov3_1_416_416.onnx  --calib-path /path/to/calibration/imgs/dir/
  • --ckpt - path to your ONNX file.
  • --calib-path - path to a directory with your calibration images in JPEG/png format
  • The model zoo will take care of adding the input normalization to be part of the model.

Note

  • On your desired YOLOv3 YAML, make sure preprocessing.input_shape fits your model's resolution.
  • For TAPPAS, retrain the model with a resolution of 608x608, and on compilation use yolov3_gluon.yaml.

More details about YAML files are presented here.