[D] Image Augmentation in Practice: In-Distribution vs OOD Augmentations, TTA, and the Manifold View

I wrote a long practical guide on image augmentation based on ~10 years of training computer vision models and ~7 years working on Albumentations.

In practice I’ve found that augmentation operates in two different regimes:

1. In-distribution augmentation Simulate realistic variation that could occur during data collection (pose, lighting, blur, noise).
2. Out-of-distribution augmentation Transforms that are intentionally unrealistic but act as regularization (extreme color jitter, grayscale, cutout, etc).

The article also discusses:

• why unrealistic augmentations can still improve generalization • how augmentation relates to the manifold hypothesis • when test-time augmentation (TTA) actually helps • common augmentation failure modes • how to design a practical baseline augmentation policy

Curious how others here approach augmentation policy design — especially with very large models.

Article: https://medium.com/data-science-collective/what-is-image-augmentation-4d31dcb3e1cc