Augmentor and albumentations
These are competitors offering overlapping image augmentation functionality, though albumentations is more actively maintained (based on its significantly higher GitHub stars) and optimized for speed with modern deep learning frameworks, while Augmentor provides a more beginner-friendly API.
About Augmentor
mdbloice/Augmentor
Image augmentation library in Python for machine learning.
Need more training data for your image-based machine learning project? This tool helps you generate diverse new images from your existing dataset by applying various transformations like rotations, zooms, and flips. It takes your original images and outputs a larger set of modified images, perfect for anyone building and training computer vision models.
About albumentations
albumentations-team/albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Provides 70+ spatial and pixel-level transforms with a unified API for multiple computer vision tasks (classification, segmentation, object detection, pose estimation) and data types (images, masks, bounding boxes, keypoints). Optimized for speed with native support for PyTorch and TensorFlow pipelines, achieving consistent benchmark performance as the fastest augmentation library. Applies transformations deterministically across all target types simultaneously, ensuring spatial consistency for tasks requiring synchronized augmentation of related annotations.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work