Shouldn't the val set use the train data augment? (but test always uses test)

Usually the val set is used to choose a model or even combine the train and val set. i.e. the val set comes from the train - test split. Also, augmentation helps for models to be better, so wouldn’t it make more sense to have the val set version that might improve the model most?

With this logic wouldn’t it make more sense to do:

        train_dataset.transform = train_data_transforms
        valid_dataset.transform = train_data_transforms
        test_dataset.transform = test_data_transforms

instead?

refs:

• https://stats.stackexchange.com/questions/320800/data-augmentation-on-training-set-only
• https://stats.stackexchange.com/questions/564146/does-it-make-sense-to-use-data-augmentation-on-the-validation-set-note-this-i?noredirect=1&lq=1

[seba-1511]

That’s a good question, I think you could make the argument for using either train or test transforms for the validation tasks. The convention in the literature is to use test transforms, so we’re sticking to that.

[brando90]

That’s a good question, I think you could make the argument for using either train or test transforms for the validation tasks. The convention in the literature is to use test transforms, so we’re sticking to that.

I agree to use test transforms on validation. It reduces the variance on the validation and since your not fitting them anyway there is likely little benefit to early stop with complicated train data augmentation for valitation. Better to have a low variance estimate of an unknown distribution so to early stop more precisely.