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Albumentations.AI

Open-source image augmentation ecosystem for computer vision. Home of AlbumentationsX, Explore, benchmarks, and the MIT-licensed Albumentations archive.

Albumentations — fast image augmentation for computer vision data pipelines

Albumentations website Documentation Explore transforms Sponsor Albumentations

Albumentations is a widely adopted open-source image-augmentation ecosystem for computer vision. It helps engineers and researchers create valid training variation while keeping images, masks, bounding boxes, keypoints, volumes, and video frames synchronized.

Start new work with AlbumentationsX

AlbumentationsX is the actively developed library in the ecosystem. Install it with OpenCV for headless servers and training environments:

pip install "albumentationsx[headless]"

The package preserves the familiar albumentations import path:

import albumentations as A

transform = A.Compose(
    [
        A.RandomCrop(height=256, width=256),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.2),
    ]
)

augmented = transform(image=image, mask=mask)

Use the documentation for guides and API details, or open Explore to test transforms in the browser.

The ecosystem

Component What it provides
AlbumentationsX Actively developed augmentation pipelines for images and synchronized annotations.
Website and documentation Project homepage, guides, API documentation, blog, benchmarks, and transform reference.
Explore Interactive browser playground for trying every transform on user-uploaded images.
Albucore Optimized atomic image-processing functions with workload-aware backend selection.
albu-spec Structured, typed metadata extracted from AlbumentationsX transforms.
Benchmark suite Reproducible image, multichannel, video, and DataLoader comparisons.
Examples Runnable notebooks for classification, segmentation, detection, keypoints, replay, serialization, and integrations.
Albumentations The MIT-licensed legacy library, preserved for reproducibility, continued use, and forks.

Public adoption

160.7M
PyPI downloads
15.3k
GitHub stars
40k+
GitHub-reported public dependents
2,270
citing research works

These figures describe public ecosystem adoption, not paid customers or endorsements. Downloads were checked on pepy.tech, stars and public dependents on GitHub, and research works in the deduplicated adoption snapshot on 22 July 2026. Browse the wider public adoption evidence. Albumentations is a NumFOCUS Affiliated Project.

Licensing

AlbumentationsX offers two licensing options:

  • Commercial license: agree on alternative rights for proprietary applications and services, with coverage for your teams, products, and customer deployments. Your agreement with Albumentations, LLC defines the scope, price, and term. Request a quote.
  • AGPL-3.0-only: available at no charge, including for commercial use, subject to its terms. Commercial or proprietary status alone does not require a purchase. Read the license guide.

The legacy albumentations package and repository remain available under the MIT License.

Join the project

Sponsorship supports maintenance; it does not include a commercial license or support agreement.

Code · Docs · Explore · Benchmarks · Sponsor

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  1. AlbumentationsX AlbumentationsX Public

    Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.

    Python 550 41

  2. albumentations albumentations Public archive

    Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

    Python 15.3k 1.7k

  3. albucore albucore Public

    A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working …

    Python 123 11

  4. benchmark benchmark Public

    Python 94 3

  5. albu-spec albu-spec Public

    Python 84

  6. albumentations_examples albumentations_examples Public

    Augmentations usage examples for albumentations library

    Python 541 102

Repositories

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People

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Sponsors

  • @datature

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