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.
Community-Driven Project, Supported By
Albumentations thrives on developer contributions. We appreciate our sponsors who help sustain the project's infrastructure.
Why Albumentations
The fastest and most flexible image augmentation library, trusted by thousands of AI engineers and researchers worldwide
Versatile
Supports classification, segmentation, detection, and more tasks out of the box
Easy to Use
Simple, intuitive API with comprehensive documentation and examples
Albumentations is a Python library for image augmentations that provides:
- Optimized performance for production environments
- Rich variety of transform operations
- Support for all major computer vision tasks
- Seamless integration with PyTorch, TensorFlow, and other frameworks
Community Feedback
Industry users of Albumentations
Different tasks
Different tasks
Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation.
Different domains
Different domains
Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks.
Seamless integration with deep learning frameworks
Seamless integration with deep learning frameworks
Albumentations can work with various deep learning frameworks such as PyTorch and Keras. The library is a part of the PyTorch ecosystem. MMDetection and YOLOv5 use Albumentations.
Getting started
Albumentations requires Python 3.9 or higher. To install the library from PyPI run
Support Open Source Development
Albumentations is a free, open-source project maintained by a dedicated team of developers
Your sponsorship helps us maintain high-quality code, provide timely updates, and develop new features
Individual Sponsors
Support open source with a monthly contribution of any size. Every dollar helps maintain and improve Albumentations.
SponsorCompany Sponsorship
Companies using Albumentations can become official sponsors, getting their logo featured on our website and documentation.
View Sponsorship TiersCiting
If you find this library useful for your research, please consider citing Albumentations: Fast and Flexible Image Augmentations:
@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}
}