convolutional neural networks epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %epoch [ / ]loss val accuracy( %with just epochswe can see that our pretrained model gives ~ accuracy over the validation dataset compared to our original model (trained from scratch)that performance improvement is significant cnn thumb rules for computer vision taskswe can delineate few rules that can be good starting points for most experiments the starting point for any given computer vision task should be leveraging pretrained network training network from scratch is always possiblebut the huge compute effortwhen results are already availablewould be futile task in scenarios where the model performance achieved is not up to your benchmarksexperiment with several other pretrained networksnot only one pytorch offers several ready-to-use pretrained models when your image classification task includes very diverse set of imagesthe pretrained networks might not give you the best performance in such casesit is recommended to unfreeze few more top layers incrementally the idea is to experiment with what level of feature representation makes sense for your