Do more with less data

Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks.

The library is widely used in industry, deep learning research, machine learning competitions, and open source projects.


PyPI Downloads

(View Dashboard)

212.6K

/ day

1.5M

/ week

6.6M

/ month

Albumentations example

Why Albumentations?

Fast & Performant

Boost model accuracy with highly optimized, benchmark-proven augmentations.

Unmatched Versatility

>100 transforms for images, masks, bounding boxes, keypoints & 3D. Used in medical, satellite, self-driving, and more.

Effortless Integration

Familiar API, similar to torchvision, for easy adoption in PyTorch, TensorFlow, and other frameworks.

Proven & Trusted

Widely adopted in research, competitions (like Kaggle), and commercial applications.

Powerful Features

Versatile Transforms

Pixel-level adjustments (brightness, contrast, noise) and spatial transformations (rotate, scale, flip).

Learn More

Task Agnostic

Consistently handles images, segmentation masks, bounding boxes, and keypoints through any augmentation pipeline.

Learn More

Performance Focused

Highly optimized code ensures minimal overhead, crucial for training large models. See benchmarks.

Learn More

Framework Agnostic

Works seamlessly with PyTorch, TensorFlow, Keras, and other frameworks, using standard NumPy arrays.

Learn More

Extensible

Easily create custom augmentations or pipelines to fit your specific research or application needs.

Learn More

Easy Serialization

Save and load augmentation pipelines using YAML or JSON for reproducibility and sharing.

Learn More

Community Feedback

Community feedback screenshot
CEO of Datature
Community feedback screenshot
Kaggle Competitions Grandmaster. Top 1 in the world.
Community feedback screenshot
Computer Vision Engineer

Community-Driven Project, Supported By

Albumentations thrives on developer contributions. We appreciate our sponsors who help sustain the project's infrastructure.

Citing

If you find Albumentations useful for your research, please consider citing the paper:

@Article{info11020125,
    AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.},
    TITLE = {Albumentations: Fast and Flexible Image Augmentations},
    JOURNAL = {Information},
    VOLUME = {11},
    YEAR = {2020},
    NUMBER = {2},
    ARTICLE-NUMBER = {125},
    URL = {https://www.mdpi.com/2078-2489/11/2/125},
    ISSN = {2078-2489},
    DOI = {10.3390/info11020125}
}

Read the paper: Information, Volume 11, Issue 2