aidevmin

> 1. **mAP**: The performance of pruned yolov7 has not been checked. > 2. **Save & Load**: Please try [tp.state_dict & tp.load_state_dict](https://github.com/VainF/Torch-Pruning/blob/master/tests/test_serialization.py). This allows us to save the attributes like...

> 1. **mAP**: The performance of pruned yolov7 has not been checked. > 2. **Save & Load**: Please try [tp.state_dict & tp.load_state_dict](https://github.com/VainF/Torch-Pruning/blob/master/tests/test_serialization.py). This allows us to save the attributes like...

@AymenBOUGUERRA Thanks for detail respone. >I dont think that parametrization is needed as exporting to onnx will apply all of the necessary optimisation on the model as well as exporting...

@AymenBOUGUERRA Thank you so much for information. >furthermore, it seems that the pruning ratio must be (1-(1/2^n)) with 0

@AymenBOUGUERRA ``` The solution that I implemented for saving while being able to reload the model is to create a yaml configuration file for the pruned model at the moment...

> Hello @chinya07, Can you please provide more context or detail your workflow ? > > Pruning conv layers(which are heavily used in YOLO architectures) will result in a complete...

@marcoslucianops thanks, custom layer you added is `nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp`?

@marcoslucianops thanks for quick and detailed response

@marcoslucianops I have 2 questions: 1. Where I can find NMS of Nvidia? 2. If my model includes NMS, as you mentioned I need to set `cluster_mode=4`, but what the...

@twangnh Thanks for response. With this repo https://github.com/wonbeomjang/yolov5-knowledge-distillation, we can not open issue. I have read this repo true, but it does not mention about GT-mask.