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albumentations-team/AlbumentationsX

Python library for image augmentation in computer vision, covering pixel, spatial, medical and 3D transforms with consistent handling of masks, boxes and keypoints.

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What is it?

AlbumentationsX is a Python library for image augmentation, the practice of generating new training samples from existing images to improve deep learning models. It provides a unified pipeline API that applies the same random transform to an image together with its masks, bounding boxes, and keypoints so the annotations stay aligned.

The library includes more than 70 pixel-level transforms such as blur, noise, color jitter, and weather effects, along with spatial transforms like crops, flips, elastic deformations, and mosaic. It also covers medical imaging transforms (for example bias field and Gibbs ringing) and 3D transforms for volumetric data. Usage examples show integration with PyTorch and TensorFlow pipelines.

The project is distributed on PyPI and requires Python 3.11 or higher. It is licensed under AGPL-3.0-only, with a commercial license available from the maintainers.

Key features

  • 01Unified API applies transforms to images, masks, bounding boxes, and keypoints together.
  • 02Over 70 pixel-level augmentations, including blur, noise, and color adjustments.
  • 03Spatial transforms such as crops, flips, affine, elastic deformation, and mosaic.
  • 04Medical imaging and 3D transforms for volumetric data.
  • 05Works with PyTorch and TensorFlow training pipelines.

Use cases

  • Expanding a small labeled dataset for object detection or segmentation training.
  • Augmenting medical scans or volumetric data while keeping masks aligned.
  • Building reproducible preprocessing pipelines for image classification models.

Quick start

  1. 1

    Install the PyTorch build for your platform (Linux CPU-only shown)

    pip install "torch>=2.13.0" --index-url https://download.pytorch.org/whl/cpu
  2. 2

    Install AlbumentationsX with OpenCV (headless variant)

    pip install "albumentationsx[headless]"
  3. 3

    Import the library and define a transform pipeline

Things to know

  • Licensed under AGPL-3.0-only, a copyleft license with source-sharing obligations for network use and derivatives.
  • Requires Python 3.11 or higher.
  • Importing the library requires a separately installed PyTorch build, which you must choose for your CPU, CUDA, or MPS setup.

Topics

#computer-vision#data-augmentation#image-processing#deep-learning#object-detection#segmentation#pytorch#3d#augmentation#bounding-box

FAQ

What is AlbumentationsX?
Python library for image augmentation in computer vision, covering pixel, spatial, medical and 3D transforms with consistent handling of masks, boxes and keypoints. AlbumentationsX is a Python library for image augmentation, the practice of generating new training samples from existing images to improve deep learning models. It provides a unified pipeline API that applies the same random transform to an image together with its masks, bounding boxes, and keypoints so the annotations stay aligned.
Is AlbumentationsX free to use? What license does it have?
AlbumentationsX is open source under the AGPL-3.0 license. Check the license terms before using it in commercial products.
Is AlbumentationsX still maintained?
Actively maintained. The last commit to AlbumentationsX was on Oct 19, 2026.
What can I use AlbumentationsX for?
Expanding a small labeled dataset for object detection or segmentation training. Augmenting medical scans or volumetric data while keeping masks aligned. Building reproducible preprocessing pipelines for image classification models.
How do I install AlbumentationsX?
Install the PyTorch build for your platform (Linux CPU-only shown): pip install "torch>=2.13.0" --index-url https://download.pytorch.org/whl/cpu Install AlbumentationsX with OpenCV (headless variant): pip install "albumentationsx[headless]" Import the library and define a transform pipeline
Where can I get AlbumentationsX?
The source code and installation instructions are on GitHub: https://github.com/albumentations-team/AlbumentationsX

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