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albumentations
  • Documentation
    • What is image augmentation
    • Why you need a dedicated library
    • 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
    • List of examples
  • FAQ
    • 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
    • Albumentations Experimental Overview
    • Installation
        • Albumentations Experimental Transforms (augmentations.transforms)
    • Blog posts, podcasts, talks, and videos about Albumentations
    • Books that mention Albumentations
  • Frameworks and libraries that use Albumentations
    • Full API Reference
      • Composition API (core.composition)
      • Transforms Interface (core.transforms_interface)
      • Serialization API (core.serialization)
      • Helper functions for working with bounding boxes (augmentations.core.bbox_utils)
      • Helper functions for working with keypoints (augmentations.core.keypoints_utils)
      • Transforms (augmentations.transforms)
        • Blur transforms (augmentations.blur.transforms)
        • Crop functional transforms (augmentations.crops.functional)
        • Crop transforms (augmentations.crops.transforms)
        • ChannelDropout augmentation (augmentations.dropout.channel_dropout)
        • CoarseDropout augmentation (augmentations.dropout.coarse_dropout)
        • Cutout augmentation (augmentations.dropout.cutout)
        • GridDropout augmentation (augmentations.dropout.grid_dropout)
        • MaskDropout augmentation (augmentations.dropout.mask_dropout)
        • 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)
      • Transforms (imgaug.transforms)
      • Index
      • Transforms (pytorch.transforms)
  • Release notes
  • Contributing

Index

  • Transforms (albumentations.pytorch.transforms)
Previous Transforms (imgaug.transforms)
Next Transforms (pytorch.transforms)