44 lines
1.1 KiB
Markdown
44 lines
1.1 KiB
Markdown
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# YOLOv5 - RKNN optimize
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## Source
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Base on https://github.com/ultralytics/yolov5 (v7.0) with commit id as 915bbf294bb74c859f0b41f1c23bc395014ea679
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## What different
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With inference result values unchanged, the following optimizations were applied:
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- Optimize focus/SPPF block, getting better performance with same result
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- Change output node, remove post_process from the model. (post process block in model is unfriendly for quantization)
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With inference result got changed, the following optimization was applied:
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- Using ReLU as activation layer instead of SiLU（Only valid when training new model）
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## How to use
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```
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# for detection model
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python export.py --rknpu --weight yolov5s.pt
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# for segmentation model
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python export.py --rknpu --weight yolov5s-seg.pt
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```
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- 'yolov5s.pt'/ 'yolov5s-seg.pt' could be replaced with your model path
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- A file name "RK_anchors.txt" would be generated and it would be used for the post_process stage.
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- **NOTICE: Please call with --rknpu, do not changing the default rknpu value in export.py.**
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## Deploy demo
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Please refer https://github.com/airockchip/rknn_model_zoo
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