Hi guys,
I am working on Traffic sign classification with the German Traffic Sign Dataset (~38k train and ~12k for test).
I implemented a model from scratch and I want to compare it to the state of the art model.
I followed the transfer learning tutorial from Pytorch tutos and tried the pre-trained models.
I am freezing all layers, except the last one.
However, the models (resnet, densenet, inception) are really slow during training (one epoch takes more than 30 mn on GPU).
From my understanding, training these models should be fast (I am working on google colab GPU).
Any help on how to train faster?