This project mainly aims at pruning YOLOv5 to see how its accuracy gets affected in parallel with different pruning percentages from 10% to 40%.
Pruning is the process of “Modification of weights by reducing the weights parameters, that does not impact on classification of classes”
Yolov5 is using technique of pruning in a way that “randomly in some percentage of weights parameters, nn.conv2d layers whose weights are nearly zero, pruning converts them to zeros”.
- Clone YOLOv5 model
- Train the model on your custom dataset
- Validate the model on a test subset using val.py file
- Copy best.py file and store it in another folder, as pruning will overwrite this file
- Edit the val.py file adding the pecentage of pruning you want before configure part as here
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from utils.torch_utils import prune prune(model,0.3) # 0.3 is pruning 30% of the weights - Validate the model again with val.py and compare the rersults