Albumentations is a Python library for fast and flexible image augmentations.

Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection.


Digital platforms: Cross-platform software

Versions: Cloud/On-Premise 


Albumentations supports all common computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation.

The library provides a simple unified API to work with all data types: images (RBG-images, grayscale images, multispectral images), segmentation masks, bounding boxes, and keypoints. The library contains more than 70 different augmentations to generate new training samples from the existing data.

Albumentations is fast. We benchmark each new release to ensure that augmentations provide maximum speed.

It works with popular deep learning frameworks such as PyTorch and TensorFlow. By the way, Albumentations is a part of the PyTorch ecosystem.

Written by experts. The authors have experience both working on production computer vision systems and participating in competitive machine learning. Many core team members are Kaggle Masters and Grandmasters.

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