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Raphael Maenle
2022-03-21 14:15:34 +00:00
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@@ -58,6 +58,7 @@ Here you'll train, quantize, compile (on a gpu if possible) and infer (on the ha
explains well on how to create that.
- There's a minimal example dataset in this repository under `/dataset`
- To mount this, use eg.: `docker run -it --gpus all -ipc=host -v /path/to/dataset/:/dataset yolov5:v0`
- `python train.py --img 640 --batch 16 --epochs 3 --data /dataset/dataset/dataset.yaml --weights yolov5m.pt --cfg models/yolov5m.yaml`
- For training, make sure you target the correct `--model` and use the correct `--weights` (which are now conveniently already in the hailo docker)
- once you've saved the best.pb onnx file, you can exit this docker container
- once you are done with the steps 'training and exporting to ONNX', move on to the next step.