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Face_Detection_YOLOv5

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”.

YOLOV5 Pruning Procedure

  • 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
  •    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