yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝，通道剪枝，知识蒸馏
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The P R F1-score and mAP@0.5 are around 90% after sparse training (--s 0.001 --prune 0). Still the sparse model is not performing well on the test dataset. #178
Hey @tanluren @zbyuan
I have trained yolov4-tiny model on a small dataset of size ~64 with augmentations (total size is 450) for a single class. For a given test dataset the original model is giving 89% P,R values.
Then I did the sparse training on the model with --s 0.001 and --prune 0 for 300 epochs. For the same test dataset the sparse model is giving 34% Recall, which is very low. I tried for s [0.01, 0.001, 0.0001] and the results are same.
Is it the epochs or the size of training dataset that is causing problem? Or there is any other issue?
I am stuck into this for a week now.
Hey @tanluren @zbyuan I have trained yolov4-tiny model on a small dataset of size ~64 with augmentations (total size is 450) for a single class. For a given test dataset the original model is giving 89% P,R values. Then I did the sparse training on the model with --s 0.001 and --prune 0 for 300 epochs. For the same test dataset the sparse model is giving 34% Recall, which is very low. I tried for s [0.01, 0.001, 0.0001] and the results are same. Is it the epochs or the size of training dataset that is causing problem? Or there is any other issue? I am stuck into this for a week now.
Any suggestions??