AlbumentationsX

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AlbumentationsX is a Python library for image augmentation. It provides high-performance, robust implementations and cutting-edge features for computer vision tasks. Image augmentation is used in deep learning and computer vision to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

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📢 Important: AlbumentationsX Licensing

AlbumentationsX offers dual licensing:

• AGPL-3.0 License: Free for open-source projects
• Commercial License: For proprietary/commercial use (contact for pricing)

Quick Start

# Install AlbumentationsX with OpenCV
pip install albumentationsx[headless]

# Or if you already have OpenCV installed
pip install albumentationsx

import albumentations as A

# Create your augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

For commercial licensing inquiries, please visit our pricing page.

Here is an example of how you can apply some pixel-level augmentations to create new images from the original one:

Why AlbumentationsX

• Complete Computer Vision Support: Works with all major CV tasks
• Simple, Unified API: One consistent interface for all data types - RGB/grayscale/multispectral images, masks, bounding boxes, and keypoints.
• Rich Augmentation Library: 70+ high-quality augmentations to enhance your training data.
• Fast: Consistently benchmarked as the fastest augmentation library also shown below section, with optimizations for production use.
• Deep Learning Integration: Works with PyTorch, TensorFlow, and other frameworks. Part of the PyTorch ecosystem.
• Created by Experts: Built by developers with deep experience in computer vision and machine learning competitions.

Table of contents

• AlbumentationsX
• Why AlbumentationsX
• Table of contents
• Authors
  • Current Maintainer
  • Emeritus Core Team Members
• Installation
• Documentation
• A simple example
• List of augmentations
  • Pixel-level transforms
  • Spatial-level transforms
• A few more examples of augmentations
  • Semantic segmentation on the Inria dataset
  • Medical imaging
  • Object detection and semantic segmentation on the Mapillary Vistas dataset
  • Keypoints augmentation
• Benchmarking results
  • System Information
  • Benchmark Parameters
  • Library Versions
• Performance Comparison
• 🤝 Contribute
• 📜 License
• 📞 Contact
• Citing

Authors

Current Maintainer

Vladimir I. Iglovikov | Kaggle Grandmaster

Emeritus Core Team Members

Mikhail Druzhinin | Kaggle Expert

Alex Parinov | Kaggle Master

Alexander Buslaev | Kaggle Master

Eugene Khvedchenya | Kaggle Grandmaster

Installation

AlbumentationsX requires Python 3.10 or higher. To install the latest version from PyPI:

Basic Installation

If you already have OpenCV installed (any variant), simply install AlbumentationsX:

pip install -U albumentationsx

Installation with OpenCV

If you don't have OpenCV installed yet, choose the appropriate variant:

# For servers/Docker (no GUI support, lighter package)
pip install -U albumentationsx[headless]

# For local development with GUI support (cv2.imshow, etc.)
pip install opencv-python && pip install -U albumentationsx

# For OpenCV with extra algorithms (contrib modules)
pip install opencv-contrib-python && pip install -U albumentationsx

# For contrib + headless
pip install -U albumentationsx[contrib-headless]

Note: AlbumentationsX works with any OpenCV variant:

• opencv-python (full version with GUI)
• opencv-python-headless (no GUI, smaller size)
• opencv-contrib-python (with extra modules)
• opencv-contrib-python-headless (contrib + headless)

Choose the one that fits your needs. The library will detect whichever is installed.

Other installation options are described in the documentation.

Documentation

The full documentation is available at https://albumentations.ai/docs/.

A simple example

import albumentations as A
import cv2

# Declare an augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Augment an image
transformed = transform(image=image)
transformed_image = transformed["image"]

AlbumentationsX collects anonymous usage statistics to improve the library. This can be disabled with ALBUMENTATIONS_OFFLINE=1 or ALBUMENTATIONS_NO_TELEMETRY=1.

List of augmentations

Pixel-level transforms

Pixel-level transforms will change just an input image and will leave any additional targets such as masks, bounding boxes, and keypoints unchanged. For volumetric data (volumes and 3D masks), these transforms are applied independently to each slice along the Z-axis (depth dimension), maintaining consistency across the volume. The list of pixel-level transforms:

• AdditiveNoise
• AdvancedBlur
• AnnotationArtifacts
• AtmosphericFog
• AutoContrast
• Blur
• CLAHE
• ChannelDropout
• ChannelShuffle
• ChannelSwap
• ChromaticAberration
• ColorJitter
• Colorize
• Defocus
• Dithering
• Downscale
• Emboss
• Enhance
• Equalize
• FDA
• FancyPCA
• FilmGrain
• FromFloat
• GaussNoise
• GaussianBlur
• GlassBlur
• HEStain
• Halftone
• HistogramMatching
• HueSaturationValue
• ISONoise
• Illumination
• ImageCompression
• InvertImg
• LensFlare
• MedianBlur
• ModeFilter
• MotionBlur
• MultiplicativeNoise
• Normalize
• PhotoMetricDistort
• PixelDistributionAdaptation
• PlanckianJitter
• PlasmaBrightnessContrast
• PlasmaShadow
• Posterize
• RGBShift
• RandomBrightnessContrast
• RandomFog
• RandomGamma
• RandomGravel
• RandomRain
• RandomShadow
• RandomSnow
• RandomSunFlare
• RandomToneCurve
• RingingOvershoot
• SaltAndPepper
• Sharpen
• ShotNoise
• Solarize
• Spatter
• Superpixels
• TextImage
• ToFloat
• ToGray
• ToRGB
• ToSepia
• UnsharpMask
• Vignetting
• ZoomBlur

Spatial-level transforms

Spatial-level transforms will simultaneously change both an input image as well as additional targets such as masks, bounding boxes, and keypoints. For volumetric data (volumes and 3D masks), these transforms are applied independently to each slice along the Z-axis (depth dimension), maintaining consist