AutoAlbument is an AutoML tool that learns image augmentation policies from data using the Faster AutoAugment algorithm. It relieves the user from the burden of manually selecting augmentations and tuning their parameters. AutoAlbument provides a complete ready-to-use configuration for an augmentation pipeline. 
The library supports image classification and semantic segmentation tasks. You can use Albumentations to utilize policies discovered by AutoAlbument in your computer vision pipelines.
AutoAlbument is MIT licensed. The source code and issue tracker are available at
The documentation is available at


AutoAlbument requires Python 3.6 or higher. To install the latest stable version from PyPI:
pip install -U autoalbument

How to use AutoAlbument

How to use AutoAlbument

  1. You need to create a configuration file with AutoAlbument parameters and a Python file that implements a custom PyTorch Dataset for your data. Next, you need to pass those files to AutoAlbument.
  2. AutoAlbument will use Generative Adversarial Network to discover augmentation policies and then create a file containing those policies.
  3. Finally, you can use Albumentations to load augmentation policies from the file and utilize them in your computer vision pipelines.
You can read the detailed description of all steps at


The examples directory in the repository contains example configs for different tasks and datasets:

AutoAlbument Roadmap

  1. Publish benchmarks of how much augmentation policies from AutoAlbument improves the quality of models trained on popular classification and semantic segmentation datasets.
  2. Add support for object detection and instance segmentation tasks.
  3. Add support for all popular augmentations available in Albumentations so AutoAlbument could use them in a search phase.

Slack #tool_albumentations

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