imgaug and fast-autoaugment

About imgaug

aleju/imgaug

Image augmentation for machine learning experiments.

Supports augmentation of diverse annotation types—heatmaps, segmentation maps, keypoints, bounding boxes, and polygons—with automatic coordinate alignment so transformations apply consistently across all data types. Provides 50+ geometric and photometric operations (affine, perspective, contrast, noise, blur) optimized for batch processing, with composable pipelines that apply random augmentations in configurable orders. Integrates seamlessly with numpy arrays and major ML frameworks through a NumPy-based architecture, enabling deterministic augmentation through seed control.

About fast-autoaugment

kakaobrain/fast-autoaugment

Official Implementation of 'Fast AutoAugment' in PyTorch.