Base on https://github.com/ultralytics/yolov5 (v6.2) with commit id as d3ea0df8b9f923685ce5f2555c303b8eddbf83fd
Inference result unchanged:
- Optimize focus/SPPF block, getting better performance with same result
- Change output node, remove post_process from the model. (post process is unfriendly in quantization)
Inference result changed:
- Using ReLU as activation layer instead of SiLU（Only valid when training new model）
python export.py --rknpu {rk_platform} --weight yolov5s.pt
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rk_platform support rk1808, rv1109, rv1126, rk3399pro, rk3566, rk3562, rk3568, rk3588, rv1103, rv1106. (Actually the exported models are the same in spite of the exact platform )
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the 'yolov5s.pt' could be replace with your model path
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A file name "RK_anchors.txt" would be generated and it could be use during doing post_process in the outside.
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NOTICE: Please call with --rknpu param, do not changing the default rknpu value in export.py.