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.

Citing

If AlbumentationsX supports your research, please cite AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations. Your citation makes the project's research impact visible to funders and helps sustain maintenance.

@article{iglovikov2026albumentationsx,
    title = {AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations},
    author = {Iglovikov, Vladimir},
    journal = {arXiv preprint arXiv:2608.11123},
    year = {2026},
    doi = {10.48550/arXiv.2608.11123},
    url = {https://arxiv.org/abs/2608.11123}
}

Licensing

AlbumentationsX offers two license options:

• Commercial license: choose alternative permissions for proprietary software under an agreement covering your team, products, and deployments. Request a quote or email vladimir@albumentations.ai.
• AGPL-3.0-only: available at no charge. The AGPL permits commercial use subject to its terms. Read the license guide.

Commercial, proprietary, internal, or production status alone does not require a commercial license. See the AGPL text and licensing details and history for the applicable terms.

For supplier questionnaires and purchasing documents, see institutional procurement.

Quick Start

# Install the PyTorch build for your platform first. For Linux CPU-only:
pip install "torch>=2.13.0" --index-url https://download.pytorch.org/whl/cpu

# Then install AlbumentationsX with OpenCV.
pip install "albumentationsx[headless]"

import albumentations as A

transform = A.Compose(
    [
        A.RandomCrop(width=256, height=256),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.2),
    ]
)

---

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

• Citing
• Licensing
• Quick Start
• Why AlbumentationsX
• Authors
• Installation
• Documentation
• A simple example
• List of augmentations
  • Pixel-level transforms
  • Spatial-level transforms
  • 3D transforms
• Augmentation examples
• Benchmark results
• Performance comparison
• Contribute
• License
• Contact
• Newsletter

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

Install the PyTorch build for your CPU, CUDA, or MPS environment before installing AlbumentationsX. For a Linux CPU-only environment:

pip install "torch>=2.13.0" --index-url https://download.pytorch.org/whl/cpu

For CUDA or macOS (MPS), use the matching command from the PyTorch installation selector. AlbumentationsX does not choose or install a PyTorch accelerator build.

If you already have OpenCV installed (any variant), 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.

pip install albumentationsx installs the base dependency set without PyTorch. It is useful for dependency-only consumers such as documentation builds. Importing albumentations requires the PyTorch build you selected above.

Other installation options are described in the documentation.

Documentation

The full documentation is available at https://albumentations.ai/docs/. For security, release verification, and contributor guides, see the repository documentation.

For AI-assisted augmentation review, AlbumentationsX can also be used through MCP-capable hosts such as Claude Desktop, Cursor, Claude Code, and Codex. The community AlbumentationsX MCP integration lets assistants inspect transforms, validate pipelines, render bounded local preview batches, compare preview runs, collect concrete feedback, and export reproducible AlbumentationsX pipelines.

A simple example

import albumentations as A
import cv2

transform = A.Compose(
    [
        A.RandomCrop(width=256, height=256),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.2),
    ]
)

image = cv2.imread("image.jpg", cv2.IMREAD_COLOR_RGB)

transformed = transform(image=image)
transformed_image = transformed["image"]

AlbumentationsX collects usage statistics by default to guide product development. See data collection and opt-out.

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 (a volume 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
• ExposureMatching
• FDA
• FancyPCA
• FilmGrain
• FromFloat
• GaussNoise
• GaussianBlur
• GlassBlur
• HEStain
• Halftone
• HistogramMatching
• HueSaturationValue
• ISONoise
• KSpaceSpikeNoise
• 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
• RicianNoise
• RingingOvershoot
• SaltAndPepper
• Sharpen
• ShotNoise
• StochasticConvolution
• 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 (a volume 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 (HBB)	BBoxes (OBB)	Keypoints	Volume	Mask3D
Affine	✓	✓	✓	✓	✓	✓	✓
AtLeastOneBBoxRandomCrop	✓	✓	✓	✓	✓	✓	✓
BBoxSafeRandomCrop	✓	✓	✓	✓	✓	✓	✓
BBoxSubsetSafeRandomCrop	✓	✓	✓	✓	✓	✓	✓
CenterCrop	✓	✓	✓	✓	✓	✓	✓
CoarseDropout	✓	✓	✓		✓	✓	✓
ConstrainedCoarseDropout	✓	✓	✓		✓	✓	✓
CopyAndPaste	✓	✓	✓		✓
Crop	✓	✓	✓	✓	✓	✓	✓
CropAndPad	✓	✓	✓	✓	✓	✓	✓
CropNonEmptyMaskIfExists	✓	✓	✓	✓	✓	✓	✓
D4	✓	✓	✓	✓	✓	✓	✓
ElasticTransform	✓	✓	✓	✓	✓	✓	✓
Erasing	✓	✓	✓		✓	✓	✓
FrequencyMasking	✓	✓	✓		✓	✓	✓
GridDistortion	✓	✓	✓	✓	✓	✓	✓
GridDropout	✓	✓	✓		✓	✓	✓
GridElasticDeform	✓	✓	✓		✓	✓	✓
GridMask	✓	✓	✓		✓	✓	✓
GuidedCoarseDropout	✓	✓	✓		✓
HorizontalFlip	✓	✓	✓	✓	✓	✓	✓
Lambda	✓	✓	✓	✓	✓	✓	✓
LetterBox	✓	✓	✓	✓	✓	✓	✓
LongestMaxSize	✓	✓	✓	✓	✓	✓	✓
MaskDropout	✓	✓	✓		✓	✓	✓
Morphological	✓	✓	✓		✓	✓	✓
Mosaic	✓	✓	✓	✓	✓
NoOp	✓	✓	✓	✓	✓	✓	✓
OpticalDistortion	✓	✓	✓	✓	✓	✓	✓
OverlayElements	✓	✓
Pad	✓	✓	✓	✓	✓	✓	✓
PadIfNeeded	✓	✓	✓	✓	✓	✓	✓
Perspective	✓	✓	✓	✓	✓	✓	✓
PiecewiseAffine	✓	✓	✓	✓	✓	✓	✓
PixelDropout	✓	✓	✓	✓	✓	✓	✓
PixelSpread	✓	✓	✓	✓	✓	✓	✓
RandomCrop	✓	✓	✓	✓	✓	✓	✓
RandomCropFromBorders	✓	✓	✓	✓	✓	✓	✓
RandomCropNearBBox	✓	✓	✓	✓	✓	✓	✓
RandomGridShuffle	✓	✓	✓		✓	✓	✓
RandomResizedCrop	✓	✓	✓	✓	✓	✓	✓
RandomRotate90	✓	✓	✓	✓	✓	✓	✓
RandomScale	✓	✓	✓	✓	✓	✓	✓
RandomSizedBBoxSafeCrop	✓	✓	✓	✓	✓	✓	✓
RandomSizedCrop	✓	✓	✓	✓	✓	✓	✓
Resize	✓	✓	✓	✓	✓	✓	✓
Rotate	✓	✓	✓	✓	✓	✓	✓
SafeRotate	✓	✓	✓	✓	✓	✓	✓
ShiftScaleRotate	✓	✓	✓	✓	✓	✓	✓
SmallestMaxSize	✓	✓	✓	✓	✓	✓	✓
SquareSymmetry	✓	✓	✓	✓	✓	✓	✓
ThinPlateSpline	✓	✓	✓	✓	✓	✓	✓
TimeMasking	✓	✓	✓		✓	✓	✓
TimeReverse	✓	✓	✓	✓	✓	✓	✓
Transpose	✓	✓	✓	✓	✓	✓	✓
VerticalFlip	✓	✓	✓	✓	✓	✓	✓
WaterRefraction	✓	✓	✓	✓	✓	✓	✓
XYMasking	✓	✓	✓		✓	✓	✓

3D transforms

3D transforms operate on volumetric data. Spatial transforms can also modify associated 3D masks and keypoints, while volume-intensity transforms leave those targets unchanged.

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
Affine3D	✓	✓	✓
Anisotropy3D	✓
CenterCrop3D	✓	✓	✓
CoarseDropout3D	✓	✓	✓
CubicSymmetry	✓	✓	✓
ElasticTransform3D	✓	✓	✓
Flip3D	✓	✓	✓
GridShuffle3D	✓	✓	✓
Pad3D	✓	✓	✓
PadIfNeeded3D	✓	✓	✓
RandomCrop3D	✓	✓	✓
RandomRotate90_3D	✓	✓	✓
Resize3D	✓	✓	✓

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

These results cover the library versions listed below.

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

See Licensing for the two options. The AGPL text, licensing history, and third-party notices record the applicable terms. Earlier releases retain the permissions that accompanied them.

📞 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, LinkedIn, and Reddit. We're here to help with all things AlbumentationsX!

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