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Albumentations is a computer vision tool that boosts the performance of deep neural networks.
The library is widely used in industry, deep learning research, machine learning competitions, and open source projects.
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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→Trusted By Leading Companies
Community Feedback
Community-Driven Project, Supported By
Albumentations thrives on developer contributions. We appreciate our supporters who help sustain the project's infrastructure.
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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