Hi I am quite new to pytorch. I was trying to implement transfer learning with CIFAR10 and resnet18 model built in. For that what I am intending to do is first download original dataset and apply some transformations onto it and the take 500 samples from each class among the 10 classes and create a new dataset with 5000 total training samples(instead of 50,000 samples in original CIFAR10).
In that case, I am just extracting and arranging the indices of the samples of the set as per their class and then randomly picking 500 of them for each class. But with that method I am not able to achieve any performance from the network even if I am doing data augmentations on the original set.
SO  is there a way to truly get the 500/per class data(using some custom dataloader) and then apply data augmentation on that to achieve the performance?
Thanks in advance!!