Training Images : 50
Augmented Images: 15000
Normally, I see papers using stand-alone augmentations. But I tried combining them. Example: Rotating -> Translating -> Zooming -> Sharpening.
I got excellent results on the test set.
But I feel the model is memorizing rather than learning.
Question: Could I have some opinions on this? Am I doing the right thing?