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albumentations
Albumentations Documentation
albumentations
Documentation
Benchmarking Results
Introduction
Introduction
What is image augmentation
Why you need a dedicated library
Why Albumentations
Getting started
Getting started
Installation
Image augmentation for classification
Mask augmentation for segmentation
Bounding boxes augmentation for object detection
Keypoints augmentation
Simultaneous augmentation of multiple targets: masks, bounding boxes, keypoints
A list of transforms and their supported targets
Setting probabilities for transforms
Examples
Examples
FAQ
External resources
External resources
Online classes that cover Albumentations
Blog posts, podcasts, talks, and videos about Albumentations
Books that mention Albumentations
Integrations
Integrations
FiftyOne integration
Frameworks and libraries that use Albumentations
API Reference
API Reference
Full API Reference
Core API
Augmentations
Augmentations
Transforms (augmentations.transforms)
Blur transforms
Crop transforms
Dropout transforms
Geometric transforms
Mixing transforms
Mixing transforms
Mixing transforms (augmentations.mixing.transforms)
Mixing functional transforms (augmentations.mixing.functional)
Mixing functional transforms (augmentations.mixing.functional)
Table of contents
albumentations.augmentations.mixing.functional
Domain adaptation transforms (augmentations.domain_adaptation)
Functional transforms (augmentations.functional)
PyTorch Helpers
AutoAlbument
AutoAlbument
Benchmarks and a comparison with baseline augmentation strategies
Installation
How to use AutoAlbument
How to use an AutoAlbument Docker image
How to use a custom classification or semantic segmentation model
Metrics and their meaning
Tuning the search parameters
Examples
Search algorithms
FAQ
Release notes
Contributing to Albumentations
Mixing functional transforms (augmentations.mixing.functional)
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