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  • Welcome to Albumentations documentation
    • What is image augmentation and how it can improve the performance of deep neural networks
    • Why you need a dedicated library for image augmentation
    • Why Albumentations
    • 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 in an augmentation pipeline
    • List of examples
    • Defining a simple augmentation pipeline for image augmentation
    • Working with non-8-bit images
    • Using Albumentations to augment bounding boxes for object detection tasks
    • How to use Albumentations for detection tasks if you need to keep all bounding boxes
    • Using Albumentations for a semantic segmentation task
    • Using Albumentations to augment keypoints
    • Applying the same augmentation with the same parameters to multiple images, masks, bounding boxes, or keypoints
    • Weather augmentations in Albumentations
    • Migrating from torchvision to Albumentations
    • PyTorch and Albumentations for image classification
    • PyTorch and Albumentations for semantic segmentation
    • Debugging an augmentation pipeline with ReplayCompose
    • How to save and load parameters of an augmentation pipeline
    • Showcase. Cool augmentation examples on diverse set of images from various real-world tasks.
    • Using Albumentations with Tensorflow
  • Frequently Asked Questions
    • AutoAlbument Overview
    • 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
      • List of examples
      • Image classification on the CIFAR10 dataset
      • Image classification on the SVHN dataset
      • Image classification on the ImageNet dataset
      • Semantic segmentation on the Pascal VOC dataset
      • Semantic segmentation on the Pascal VOC dataset
    • Search algorithms
    • FAQ
    • AutoAlbument introduction and core concepts
    • Albumentations Experimental Overview
    • Installation
        • Albumentations Experimental Transforms (augmentations.transforms)
    • Blog posts, podcasts, talks, and videos about Albumentations
    • Books that mention Albumentations
      • Index
      • Composition API (core.composition)
      • Transforms Interface (core.transforms_interface)
      • Serialization API (core.serialization)
      • Transforms (augmentations.transforms)
        • Crop functional transforms (augmentations.crops.functional)
        • Crop transforms (augmentations.crops.transforms)
        • Geometric functional transforms (augmentations.geometric.functional)
        • Resizing transforms (augmentations.geometric.resize)
        • Rotation transforms (augmentations.geometric.functional)
        • Geometric transforms (augmentations.geometric.transforms)
      • Domain adaptation transforms (augmentations.domain_adaptation)
      • Functional transforms (augmentations.functional)
      • Helper functions for working with bounding boxes (augmentations.bbox_utils)
      • Helper functions for working with keypoints (augmentations.keypoints_utils)
      • Geometric augmentations (augmentations.geometric)
      • Transforms (imgaug.transforms)
      • Transforms (pytorch.transforms)
  • Release notes
  • Contributing

Index

  • Composition API (albumentations.core.composition)
  • Serialization API (albumentations.core.serialization)
  • Transforms Interface (albumentations.core.transforms_interface)
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