Albumentations

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

From albumentations-team·Updated May 30, 2026·View on GitHub··Archived

> 📣 **Stay updated!** [Subscribe to our newsletter](https://albumentations.ai/subscribe) for the latest releases, tutorials, and tips directly from the Albumentations team. The project is written primarily in Python, distributed under the MIT License license, first published in 2018. It has gained significant community traction with 15,303 stars and 1,708 forks on GitHub. Key topics include: augmentation, deep-learning, detection, fast-augmentations, image-augmentation.

Latest release: 2.0.8— 🛠 Albumentations 2.0.8 Release Notes

May 27, 2025View Changelog →

Albumentations

📣 Stay updated! Subscribe to our newsletter for the latest releases, tutorials, and tips directly from the Albumentations team.

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⚠️ Important Notice: Albumentations is No Longer Maintained

This repository is no longer actively maintained. The last update was in June 2025, and no further bug fixes, features, or compatibility updates will be provided.

🚀 Introducing AlbumentationsX - The Future of Albumentations

All development has moved to AlbumentationsX, the next-generation successor to Albumentations.

Note: AlbumentationsX uses dual licensing (AGPL-3.0 / Commercial). The AGPL license has strict copyleft requirements - see details below.

Your Options Moving Forward

1. Continue Using Albumentations (MIT License)

✅ Forever free for all uses including commercial
✅ No licensing fees or restrictions
❌ No bug fixes - Even critical bugs won't be addressed
❌ No new features - Missing out on performance improvements
❌ No support - Issues and questions go unanswered
❌ No compatibility updates - May break with new Python/PyTorch versions

Best for: Projects that work fine with the current version and don't need updates

2. Upgrade to AlbumentationsX (Dual Licensed)

✅ Drop-in replacement - Same API, just pip install albumentationsx
✅ Active development - Regular updates and new features
✅ Bug fixes - Issues are actively addressed
✅ Performance improvements - Faster execution
✅ Community support - Active Discord and issue tracking
⚠️ Dual licensed:
- AGPL-3.0: Free ONLY for projects licensed under AGPL-3.0 (not compatible with MIT, Apache, BSD, etc.)
- Commercial License: Required for proprietary use AND permissive open-source projects

Best for: Projects that need ongoing support, updates, and new features

⚠️ AGPL License Warning: The AGPL-3.0 license is NOT compatible with permissive licenses like MIT, Apache 2.0, or BSD. If your project uses any of these licenses, you CANNOT use the AGPL version of AlbumentationsX - you'll need a commercial license.

Migration is Simple

bash
# Uninstall original
pip uninstall albumentations

# Install AlbumentationsX
pip install albumentationsx

That's it! Your existing code continues to work without any changes:

python
import albumentations as A  # Same import!

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

Learn More

📦 AlbumentationsX Repository: https://github.com/albumentations-team/AlbumentationsX
💰 Commercial Licensing: https://albumentations.ai/pricing
💬 Discord Community: https://discord.gg/AKPrrDYNAt

Original Albumentations README

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Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

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

Why Albumentations

Complete Computer Vision Support: Works with all major CV tasks including classification, segmentation (semantic & instance), object detection, and pose estimation.
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, 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.

Albumentations

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

Albumentations requires Python 3.9 or higher. To install the latest version from PyPI:

bash
pip install -U albumentations

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"]

Getting started

I am new to image augmentation

Please start with the introduction articles about why image augmentation is important and how it helps to build better models.

I want to use Albumentations for the specific task such as classification or segmentation

If you want to use Albumentations for a specific task such as classification, segmentation, or object detection, refer to the set of articles that has an in-depth description of this task. We also have a list of examples on applying Albumentations for different use cases.

I want to explore augmentations and see Albumentations in action

Check the online demo of the library. With it, you can apply augmentations to different images and see the result. Also, we have a list of all available augmentations and their targets.

Who is using Albumentations

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:

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

inria

Medical imaging

medical

Object detection and semantic segmentation on the Mapillary Vistas dataset

vistas

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

albumentations: 2.0.4
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 Albumentations is compared to the fastest other
library for each transform.

Transform	albumentations<br>2.0.4	augly<br>1.0.0	imgaug<br>0.4.0	kornia<br>0.8.0	torchvision<br>0.20.1	Speedup<br>(Alb/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

Contributing

To create a pull request to the repository, follow the documentation at CONTRIBUTING.md

Community

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}
}