Dog Breed Identification challenge

[jamesrequa]

@sermakarevich

I precomputed them once: for train (train - 1 image), validation [1 image] and test. And than just changed indexes and never precompute activations once again.

OK I think I got it now and you were able to do this by following your steps you posted in this thread linked below right?

Cross Validation with fast.ai

Hey All. no intro, just sharing how to do CV predictions with fastai for train and test sets. Why not with ImageClassifierData.from_csv ? - well, because it gonna precompute activations every time you change folds both for train and for test. Obviously it should be enough to precompute activations once and than just change train/validation sets based on CV indexes. So our steps: Precompute activations once: data = ImageClassifierData.from_csv(val_idxs =[0], test_name='test') learn = ConvL…

So basically you precomputed activations on all of the data (except one image) and then you just changed the indexes to split up train/validation sets…right? Although you had to create a custom function to be able to do this in fastai it seems