imgaug and Augmentor
These are competitors offering overlapping functionality for image augmentation pipelines—both provide transformation libraries for training data, though imgaug dominates in adoption and API flexibility while Augmentor emphasizes simplicity and GUI-based workflows.
About imgaug
aleju/imgaug
Image augmentation for machine learning experiments.
This tool helps machine learning engineers and researchers expand their image datasets by creating many altered versions of original images. You feed it a set of input images, along with associated data like heatmaps, segmentation maps, keypoints, or bounding boxes. It then outputs a much larger collection of subtly modified images and their corresponding updated annotations, making your models more robust.
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.
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