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

```bash

Install AlbumentationsX

pip install albumentationsx ```

```python 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 3.9 or higher. To install the latest version from PyPI:

bash pip install -U albumentationsx

Other installation options are described in the documentation.

Documentation

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

A simple example

```python 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
• AutoContrast
• Blur
• CLAHE
• ChannelDropout
• ChannelShuffle
• ChromaticAberration
• ColorJitter
• Defocus
• Dithering
• Downscale
• Emboss
• Equalize
• FDA
• FancyPCA
• FromFloat
• GaussNoise
• GaussianBlur
• GlassBlur
• HEStain
• HistogramMatching
• HueSaturationValue
• ISONoise
• Illumination
• ImageCompression
• InvertImg
• MedianBlur
• MotionBlur
• MultiplicativeNoise
• Normalize
• 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
• 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 consistency across the volume. The following table shows which additional targets are supported by each transform:

• Volume: 3D array of shape (D, H, W) or (D, H, W, C) where D is depth, H is height, W is width, and C is number of channels (optional)
• Mask3D: Binary or multi-class 3D mask of shape (D, H, W) where each slice represents segmentation for the corresponding volume slice

| Transform | Image | Mask | BBoxes | Keypoints | Volume | Mask3D | | ------------------------------------------------------------------------------------------------ | :---: | :--: | :----: | :-------: | :----: | :----: | | Affine | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | AtLeastOneBBoxRandomCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | BBoxSafeRandomCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | CenterCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | CoarseDropout | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | ConstrainedCoarseDropout | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Crop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | CropAndPad | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | CropNonEmptyMaskIfExists | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | D4 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | ElasticTransform | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Erasing | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | FrequencyMasking | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | GridDistortion | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | GridDropout | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | GridElasticDeform | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | HorizontalFlip | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Lambda | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | LongestMaxSize | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | MaskDropout | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Morphological | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Mosaic | ✓ | ✓ | ✓ | ✓ | | | | NoOp | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | OpticalDistortion | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | OverlayElements | ✓ | ✓ | | | | | | Pad | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | PadIfNeeded | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Perspective | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | PiecewiseAffine | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | PixelDropout | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomCropFromBorders | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomCropNearBBox | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomGridShuffle | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomResizedCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomRotate90 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomScale | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomSizedBBoxSafeCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | RandomSizedCrop | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Resize | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Rotate | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | SafeRotate | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | ShiftScaleRotate | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | SmallestMaxSize | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | SquareSymmetry | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | ThinPlateSpline | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | TimeMasking | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | TimeReverse | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | Transpose | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | VerticalFlip | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | XYMasking | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

3D transforms

3D transforms operate on volumetric data and can modify both the input volume and associated 3D mask.

Where:

• Volume: 3D array of shape (D, H, W) or (D, H, W, C) where D is depth, H is height, W is width, and C is number of channels (optional)
• Mask3D: Binary or multi-class 3D mask of shape (D, H, W) where each slice represents segmentation for the corresponding volume slice

| Transform | Volume | Mask3D | Keypoints | | ------------------------------------------------------------------------------ | :----: | :----: | :-------: | | CenterCrop3D | ✓ | ✓ | ✓ | | CoarseDropout3D | ✓ | ✓ | ✓ | | CubicSymmetry | ✓ | ✓ | ✓ | | Pad3D | ✓ | ✓ | ✓ | | PadIfNeeded3D | ✓ | ✓ | ✓ | | RandomCrop3D | ✓ | ✓ | ✓ |

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

Benchmark Results

Image Benchmark Results

System Information

• Platform: macOS-15.1-arm64-arm-64bit
• Processor: arm
• CPU Count: 16
• Python Version: 3.12.8

Benchmark Parameters

• Number of images: 2000
• Runs per transform: 5
• Max warmup iterations: 1000

Library Versions

• albumentationsx: 2.0.8
• augly: 1.0.0
• imgaug: 0.4.0
• kornia: 0.8.0
• torchvision: 0.20.1

Performance Comparison

Number shows how many uint8 images per second can be processed on one CPU thread. Larger is better. The Speedup column shows how many times faster AlbumentationsX is compared to the fastest other library for each transform.

| Transform | albumentationsx
2.0.8 | augly
1.0.0 | imgaug
0.4.0 | kornia
0.8.0 | torchvision
0.20.1 | Speedup
(AlbX/fastest other) | |:---------------------|:--------------------------|:-----------------|:------------------|:------------------|:------------------------|:---------------------------------| | Affine | 1445 ± 9 | - | 1328 ± 16 | 248 ± 6 | 188 ± 2 | 1.09x | | AutoContrast | 1657 ± 13 | - | - | 541 ± 8 | 344 ± 1 | 3.06x | | Blur | 7657 ± 114 | 386 ± 4 | 5381 ± 125 | 265 ± 11 | - | 1.42x | | Brightness | 11985 ± 455 | 2108 ± 32 | 1076 ± 32 | 1127 ± 27 | 854 ± 13 | 5.68x | | CLAHE | 647 ± 4 | - | 555 ± 14 | 165 ± 3 | - | 1.17x | | CenterCrop128 | 119293 ± 2164 | - | - | - | - | N/A | | ChannelDropout | 11534 ± 306 | - | - | 2283 ± 24 | - | 5.05x | | ChannelShuffle | 6772 ± 109 | - | 1252 ± 26 | 1328 ± 44 | 4417 ± 234 | 1.53x | | CoarseDropout | 18962 ± 1346 | - | 1190 ± 22 | - | - | 15.93x | | ColorJitter | 1020 ± 91 | 418 ± 5 | - | 104 ± 4 | 87 ± 1 | 2.44x | | Contrast | 12394 ± 363 | 1379 ± 25 | 717 ± 5 | 1109 ± 41 | 602 ± 13 | 8.99x | | CornerIllumination | 484 ± 7 | - | - | 452 ± 3 | - | 1.07x | | Elastic | 374 ± 2 | - | 395 ± 14 | 1 ± 0 | 3 ± 0 | 0.95x | | Equalize | 1236 ± 21 | - | 814 ± 11 | 306 ± 1 | 795 ± 3 | 1.52x | | Erasing | 27451 ± 2794 | - | - | 1210 ± 27 | 3577 ± 49 | 7.67x | | GaussianBlur | 2350 ± 118 | 387 ± 4 | 1460 ± 23 | 254 ± 5 | 127 ± 4 | 1.61x | | GaussianIllumination | 720 ± 7 | - | - | 436 ± 13 | - | 1.65x | | GaussianNoise | 315 ± 4 | - | 263 ± 9 | 125 ± 1 | - | 1.20x | | Grayscale | 32284 ± 1130 | 6088 ± 107 | 3100 ± 24 | 1201 ± 52 | 2600 ± 23 | 5.30x | | HSV | 1197 ± 23 | - | - | - | - | N/A | | HorizontalFlip | 14460 ± 368 | 8808 ± 1012 | 9599 ± 495 | 1297 ± 13 | 2486 ± 107 | 1.51x | | Hue | 1944 ± 64 | - | - | 150 ± 1 | - | 12.98x | | Invert | 27665 ± 3803 | - | 3682 ± 79 | 2881 ± 43 | 4244 ± 30 | 6.52x | | JpegCompression | 1321 ± 33 | 1202 ± 19 | 687 ± 26 | 120 ± 1 | 889 ± 7 | 1.10x | | LinearIllumination | 479 ± 5 | - | - | 708 ± 6 | - | 0.68x | | MedianBlur | 1229 ± 9 | - | 1152 ± 14 | 6 ± 0 | - | 1.07x | | MotionBlur | 3521 ± 25 | - | 928 ± 37 | 159 ± 1 | - | 3.79x | | Normalize | 1819 ± 49 | - | - | 1251 ± 14 | 1018 ± 7 | 1.45x | | OpticalDistortion | 661 ± 7 | - | - | 174 ± 0 | - | 3.80x | | Pad | 48589 ± 2059 | - | - | - | 4889 ± 183 | 9.94x | | Perspective | 1206 ± 3 | - | 908 ± 8 | 154 ± 3 | 147 ± 5 | 1.33x | | PlankianJitter | 3221 ± 63 | - | - | 2150 ± 52 | - | 1.50x | | PlasmaBrightness | 168 ± 2 | - | - | 85 ± 1 | - | 1.98x | | PlasmaContrast | 145 ± 3 | - | - | 84 ± 0 | - | 1.71x | | PlasmaShadow | 183 ± 5 | - | - | 216 ± 5 | - | 0.85x | | Posterize | 12979 ± 1121 | - | 3111 ± 95 | 836 ± 30 | 4247 ± 26 | 3.06x | | RGBShift | 3391 ± 104 | - | - | 896 ± 9 | - | 3.79x | | Rain | 2043 ± 115 | - | - | 1493 ± 9 | - | 1.37x | | RandomCrop128 | 111859 ± 1374 | 45395 ± 934 | 21408 ± 622 | 2946 ± 42 | 31450 ± 249 | 2.46x | | RandomGamma | 12444 ± 753 | - | 3504 ± 72 | 230 ± 3 | - | 3.55x | | RandomResizedCrop | 4347 ± 37 | - | - | 661 ± 16 | 837 ± 37 | 5.19x | | Resize | 3532 ± 67 | 1083 ± 21 | 2995 ± 70 | 645 ± 13 | 260 ± 9 | 1.18x | | Rotate | 2912 ± 68 | 1739 ± 105 | 2574 ± 10 | 256 ± 2 | 258 ± 4 | 1.13x | | SaltAndPepper | 629 ± 6 | - | - | 480 ± 12 | - | 1.31x | | Saturation | 1596 ± 24 | - | 495 ± 3 | 155 ± 2 | - | 3.22x | | Sharpen | 2346 ± 10 | - | 1101 ± 30 | 201 ± 2 | 220 ± 3 | 2.13x | | Shear | 1299 ± 11 | - | 1244 ± 14 | 261 ± 1 | - | 1.04x | | Snow | 611 ± 9 | - | - | 143 ± 1 | - | 4.28x | | Solarize | 11756 ± 481 | - | 3843 ± 80 | 263 ± 6 | 1032 ± 14 | 3.06x | | ThinPlateSpline | 82 ± 1 | - | - | 58 ± 0 | - | 1.41x | | VerticalFlip | 32386 ± 936 | 16830 ± 1653 | 19935 ± 1708 | 2872 ± 37 | 4696 ± 161 | 1.62x |

🤝 Contribute

We thrive on community collaboration! AlbumentationsX wouldn't be the powerful augmentation library it is without contributions from developers like you. Please see our Contributing Guide to get started. A huge Thank You 🙏 to everyone who contributes!

We look forward to your contributions to help make the AlbumentationsX ecosystem even better!

📜 License

AlbumentationsX offers two licensing options to suit different needs:

• AGPL-3.0 License: This OSI-approved open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the LICENSE file for full details.
• AlbumentationsX Commercial License: Designed for commercial use, this license allows for the seamless integration of AlbumentationsX into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please visit our pricing page.

📞 Contact

For bug reports and feature requests related to AlbumentationsX, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Twitter, and LinkedIn. We're here to help with all things AlbumentationsX!

Citing

If you find this library useful for your research, please consider citing Albumentations: Fast and Flexible Image Augmentations:

bibtex @Article{info11020125, AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.}, TITLE = {Albumentations: Fast and Flexible Image Augmentations}, JOURNAL = {Information}, VOLUME = {11}, YEAR = {2020}, NUMBER = {2}, ARTICLE-NUMBER = {125}, URL = {https://www.mdpi.com/2078-2489/11/2/125}, ISSN = {2078-2489}, DOI = {10.3390/info11020125} }

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