I mentioned that I don’t think any of the examples are clean.
Also, if the random rotations/distortions/etc. are used per-sample, does that mean that the original sample could potentially never be used for training? In keras, the augmentation produces additional samples. Is this not the case for pytorch? In other words, is there any way to train on the original sample, as well as whatever transformations I want to apply to the data? For context, I don’t want to concatenate a transformed/modified dataset to the original dataset prior to training