aleju/imgaug

Health v2 · maintenance only

Adoption not part of the score

14741 stars · 2451 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

imgaug is a Python library for augmenting images in machine learning experiments, converting a small set of input images into a much larger set of slightly altered versions. It supports a wide range of augmentation techniques and can consistently augment not only images but also keypoints, bounding boxes, heatmaps, and segmentation maps.

Use cases

• augment images for training deep learning models
• generate more training data from a small image dataset
• augment bounding boxes and keypoints alongside images
• augment segmentation maps and heatmaps consistently with images
• reduce overfitting in CNN training with varied images
• apply random affine transformations, crops, and contrast changes to images

When to choose

• you need image augmentation that keeps labels like bounding boxes, keypoints, and segmentation maps in sync
• you want a large catalog of ready-made augmenters (weather, blur, contrast, geometric) combinable in pipelines
• you need stochastic, reproducible augmentation with multi-core execution

When to avoid

• you need GPU-accelerated augmentation tightly integrated with a modern framework like PyTorch or TensorFlow (consider albumentations or built-in pipeline transforms)
• you need active development and new features, as the project is largely in maintenance
• you augment non-image modalities such as text or audio

Facets

library · maturity maintenance

image-processing machine-learning data-generation machine-learning computer-vision deep-learning image-processing python cross-platform image-augmentation data-augmentation bounding-boxes keypoints segmentation-maps heatmaps computer-vision deep-learning

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markdown · JSON · MCP: product_card(name="aleju/imgaug")