Augmentation

Augmentation makes new training images by flipping, rotating, cropping or recolouring the ones you have.

• The model sees slightly different versions each epoch, so it generalises better.
• Only use changes that keep the label true.
• Augment training data only, never the test set.

import numpy as np
img = np.array([[1, 2, 3], [4, 5, 6]])
print(np.fliplr(img).tolist())

[[3, 2, 1], [6, 5, 4]]

Remember: Ask: would a person still give it the same label?

Common mistake: Flipping digits upside down: a 6 becomes a 9.