Welcome to Albumentations Documentation! 🔗

Albumentations is a fast and flexible library for image augmentation. Whether you're working on classification, segmentation, object detection, or other computer vision tasks, Albumentations provides a comprehensive set of transforms and a powerful pipeline framework.

This documentation will guide you through installing the library, understanding its core concepts, applying it to various tasks, and exploring advanced features.

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
  • Object Detection (Bounding Boxes)
  • Keypoint Augmentation
  • Video Augmentation
  • Volumetric (3D) Augmentation
• 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
  • Serialization

Other Resources 🔗

• Comparing with Torchvision/Kornia: See how Albumentations compares to other libraries.
• Frequently Asked Questions (FAQ): Find answers to common questions.
• Benchmarks: Performance comparison results.
• Supported Targets by Transform: Check which transforms work with images, masks, bounding boxes, keypoints, etc.
• API Reference
• GitHub Repository: Source code, issue tracking, and contributions.
• Examples Folder (on GitHub): Many practical examples in the main repository.

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