Albumentations vs Kornia: Small Dataset Augmentation Guide
TildAliceWhy Your 200-Image Dataset Isn't Doomed Small datasets are the norm, not the exception....
Why Your 200-Image Dataset Isn't Doomed
Small datasets are the norm, not the exception. You've got 200 labeled medical images, or 500 product photos, and you need to train a classifier that doesn't just memorize the training set. Data augmentation is the obvious move, but the implementation details matter more than most tutorials admit.
I tested two popular augmentation libraries — Albumentations and Kornia — on a 300-image dataset of industrial defects. One crashed my training loop with CUDA errors. The other added 40% to my epoch time. Here's what actually happened.
The Libraries: CPU-First vs GPU-Native
Albumentations builds on NumPy and OpenCV. Every transform runs on CPU, outputs a NumPy array, and you convert to tensor afterward. It's been the go-to choice since 2018 because the API is clean and the transform catalog is huge — 70+ operations including domain-specific stuff like CLAHE and optical distortion.
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