kakaobrain/fast-autoaugment

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1605 stars · 197 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of Fast AutoAugment (NeurIPS 2019), which learns image augmentation policies via density-matching search. It reduces augmentation policy search time by orders of magnitude compared to AutoAugment while achieving comparable accuracy.

Use cases

• search for image augmentation policies for CIFAR-10/100 training
• speed up AutoAugment policy search on ImageNet
• improve image classification accuracy with learned augmentation
• reproduce Fast AutoAugment paper results
• apply learned augmentation policies to CNN training in PyTorch

When to choose

• you train CNNs in PyTorch and want AutoAugment-quality policies with far less search compute
• you need the official reference implementation or pretrained checkpoints from the paper

When to avoid

• you use TensorFlow or non-PyTorch frameworks
• you need actively maintained tooling - the repo has not seen releases since 2021
• you work outside image classification, e.g. NLP or detection

Facets

library · maturity maintenance

machine-learning image-processing data-science deep-learning computer-vision image-processing python autoaugment data-augmentation automl pytorch image-classification neurips-2019 cnn gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="kakaobrain/fast-autoaugment")