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    • Documentation
    • Benchmarking Results
      • 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
      • Transform Library Comparison Guide
      • Setting probabilities for transforms
    • Examples
    • FAQ
      • Online classes that cover Albumentations
      • Blog posts, podcasts, talks, and videos about Albumentations
      • Books that mention Albumentations
      • HuggingFace
      • Roboflow
      • Voxel51
    • Frameworks and libraries that use Albumentations
    • API Reference
      • Full API Reference
      • Core API
      • Augmentations
        • Transforms (augmentations.transforms)
        • Blur transforms
        • Crop transforms
        • Dropout transforms
        • Geometric transforms
        • Domain Adaptation transforms
        • Functional transforms (augmentations.functional)
      • PyTorch Helpers
    • Contributing
      • Coding Guidelines
      • Environment Setup
    • 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

    Index

    • Mixing transforms (augmentations.mixing.transforms)
    • Mixing functional transforms (albumentations.augmentations.mixing.functional)
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    Geometric transforms (augmentations.geometric.transforms)
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    Mixing transforms (augmentations.mixing.functional)