Welcome to Albumentations Documentation!

On this page

• Getting Started
• Learning the Basics
• Advanced Topics
• Other Resources

Albumentations is the most obvious default augmentation library for most computer vision users: fast pipelines, a broad transform catalog, and target-aware support for classification, segmentation, object detection, pose, OCR, medical, remote-sensing, video, and 3D workloads. Use it for training augmentation, test-time augmentation, validation diagnostics, preprocessing experiments, and any image/video/volume augmentation policy that needs to be correct, inspectable, and easy to integrate.

Albumentations works cleanly with PyTorch, TensorFlow/Keras, JAX, and custom training stacks. In GPU training, it commonly runs before tensors enter the framework-specific model step, for example inside PyTorch Dataset or DataLoader workers; you keep your framework for models, tensors, training loops, and deployment.

Install with pip install albumentationsx. See the License Guide for licensing (AGPL/Commercial).

This documentation will guide you through installing the library, understanding its core concepts, applying it to real training pipelines, and optimizing augmentation for correctness and throughput.

Getting Started

• Introduction: Learn what data augmentation is and why it's important.
• Installation: Set up Albumentations in your environment.

Learning the Basics

• Core Concepts: Understand the fundamental building blocks: Transforms, Pipelines (Compose), Targets (image, mask, bboxes, keypoints), and Probabilities.
• Basic Usage Guides: Find practical examples for common computer vision tasks:
  • Image Classification
  • Semantic Segmentation
  • Instance Segmentation
  • Object Detection (Bounding Boxes)
  • Oriented Bounding Boxes (OBB)
  • Keypoint Augmentation
  • Video Augmentation
  • Volumetric (3D) Augmentation
  • Framework Integrations: Use Albumentations with PyTorch, TensorFlow/Keras, JAX, and custom training loops.
• Choosing Augmentations: A detailed guide on selecting effective augmentation strategies for model generalization.
• Performance Tuning: Tips for optimizing your augmentation pipeline speed.

Advanced Topics

• Advanced Guides: Explore more complex features:
  • Using Additional Targets
  • Creating Custom Transforms
  • Reproducibility in Albumentations
  • Replay and Applied-Parameter Debugging
  • Serialization
  • Test Time Augmentation (TTA)

Other Resources

• Comparisons:
  • Albumentations vs PIL/Pillow: Compare Albumentations with Pillow for RGB image editing, arbitrary-channel arrays, and target-aware augmentation.
  • Albumentations vs torchvision: Why Albumentations is the default PyTorch Dataset / DataLoader augmentation layer, and where torchvision still fits.
  • Albumentations vs Kornia: Compare the default CPU/DataLoader path with Kornia's narrower differentiable and GPU tensor augmentation use cases.
  • Albumentations vs DALI: Compare Albumentations with NVIDIA DALI for graph-style GPU decode and preprocessing pipelines.
  • Legacy combined torchvision/Kornia mapping: Compatibility landing page for the old combined guide.
• AlbumentationsX vs Legacy Albumentations: Practical Benefits: Technical comparison for teams evaluating the legacy albumentations package against AlbumentationsX.
• Security, Safety, and Release Integrity: Vulnerability reporting, release verification, trusted publishing, and operational continuity.
• Frequently Asked Questions (FAQ): Find answers to common questions.
• Benchmarks: Performance comparison results and benchmark methodology.
• Supported Targets by Transform: Check which transforms work with images, masks, bounding boxes, keypoints, etc.
• API Reference
• GitHub Repository: Active development
• Examples Repository: Many practical examples.

We hope this documentation helps you leverage the full power of Albumentations!