albumentations-team/AlbumentationsX

Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.

What it solves

AlbumentationsX addresses the need for high-performance image augmentation in computer vision. It helps deep learning practitioners increase the quality of trained models by creating new, synthetic training samples from existing data, reducing overfitting and improving model robustness.

How it works

It provides a Python library with a unified API that allows users to compose pipelines of image transformations. These transforms can be applied to various data types, including RGB, grayscale, and multispectral images, as well as their associated annotations like masks, bounding boxes, and keypoints. It integrates with major deep learning frameworks such as PyTorch and TensorFlow and leverages OpenCV for efficient image processing.

Who it’s for

This tool is designed for computer vision engineers, machine learning researchers, and data scientists who need to expand their training datasets for deep learning tasks.

Highlights

  • Extensive Library: Includes over 70 high-quality augmentations, categorized into pixel-level, spatial-level, and 3D transforms.
  • Unified Interface: A single API handles images and related annotations (masks, boxes, keypoints) simultaneously.
  • High Performance: Benchmarked as one of the fastest augmentation libraries available, optimized for production use.
  • Broad Compatibility: Works across all major CV tasks and integrates with the PyTorch ecosystem.

Related

  • Dispatch
    Hugging Face TextImage Augmentation pipeline release

    Hugging Face and Albumentations AI released a TextImage Augmentation pipeline that jointly modifies document images and their text, enabling realistic synthetic data generation and robust fine‑tuning of vision‑language models on limited document datasets.

  • Project
    iver56/audiomentations

    A Python library for audio data augmentation that provides a wide variety of transforms to make audio deep learning models more robust for real-world use.

  • Project
    jacobgil/pytorch-grad-cam

    A PyTorch library for explainable AI in computer vision that provides a wide range of pixel attribution methods to visualize and diagnose model predictions.

  • Project
    CVHub520/X-AnyLabeling

    A unified cross-platform desktop application for AI-assisted annotation of text, image, video, and multimodal data, integrating state-of-the-art deep learning models to automate labeling.

  • Project
    TechyNilesh/DeepImageSearch

    A Python library for building AI-powered image search systems supporting text, image, and hybrid search using multimodal embeddings and vector indexing.