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Using YOLOv8 Retraining Docker

In this example, we’re going to retrain the model to detect barcodes, using the barcode-detector dataset from Kaggle. After the retraining process, we’re going to convert the model to HEF and test it on the Raspberry Pi 5 AI Kit.

This tutorial was made on a development machine with this spec

Hardware:

  • CPU: Intel i7-6850K
  • GPU: RTX 4080

Software:

  • OS: Ubuntu 20.04
  • Hailo DFC version: 3.27.0
  • Hailo Model-Zoo: 2.11.0

On the development machine

  1. Install the Hailo AI SW-Suite from the Developer Zone. Alternatively, you can download and install the DFC and the model-zoo into the same virtual environment.
  2. Follow the instructions on the YOLOv8 retraining page: YOLOv8 Retraining
  3. Note in this example we added volume mount with the name data to the Docker container.
  4. Download the barcode-detector dataset from Kaggle. Make sure that it’s either mapped into the retraining Docker, or copied inside.

Launch the retraining

On my RTX 4080, it took about 3 hours:

yolo detect train data=/data/barcode-detect/data.yaml model=yolov8s.pt name=retrain_yolov8s epochs=20 batch=8

After the final epoch has finished, you should see a message like this: final-epoch

Validate the new checkpoint

yolo predict task=detect source=/data/barcode-detect/valid/images/05102009190_jpg.rf.e9661dd52bd50001b08e7a510978560b.jpg model=./runs/detect/retrain_yolov8s/weights/best.pt

Expected output: validate-model

Export the model to ONNX

yolo export model=/workspace/ultralytics/runs/detect/retrain_yolov8s/weights/best.pt imgsz=640 format=onnx opset=11

Copy the ONNX to a directory mapped outside the Docker container

cp ./runs/detect/retrain_yolov8s/weights/best.onnx /data/barcode-detection.onnx

Exit the Docker

Convert the model to Hailo

Use the Hailo Model Zoo command (this can take up to 30 minutes):

hailomz compile yolov8s --ckpt=barcode-detection.onnx --hw-arch hailo8l --calib-path barcode-detect/test/images/ --classes 2 --performance

You should get a message like this:  successful-compilation

Now, we have the yolov8s.hef. This file can be used on the Raspberry Pi 5 AI Kit.

For deployment instructions, see Using Retrained Models.