albumentations 2.0.8

Fast, flexible, and advanced augmentation library for deep learning, computer vision, and medical imaging. Albumentations offers a wide range of transformations for both 2D (images, masks, bboxes, keypoints) and 3D (volumes, volumetric masks, keypoints) data, with optimized performance and seamless integration into ML workflows.

Albumentations

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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:

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 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.

Community-Driven Project, Supported By

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Table of contents

• Albumentations
  • Why Albumentations
  • Community-Driven Project, Supported By
    • 💝 Become a Sponsor
  • Table of contents
  • Authors
    • Current Maintainer
    • Emeritus Core Team Members
  • Installation
  • Documentation
  • A simple example
  • Getting started
    • I am new to image augmentation
    • I want to use Albumentations for the specific task such as classification or segmentation
    • I want to know how to use Albumentations with deep learning frameworks
    • I want to explore augmentations and see Albumentations in action
  • Who is using Albumentations
    • See also
  • 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
  • Contributing
  • Community
  • 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

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

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

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 know how to use Albumentations with deep learning frameworks

We have examples of using Albumentations along with PyTorch and TensorFlow.

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

See also

• A list of papers that cite Albumentations.
• Open source projects that use 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:

• AdditiveNoise
• AdvancedBlur
• AutoContrast
• Blur
• CLAHE
• ChannelDropout
• ChannelShuffle
• ChromaticAberration
• ColorJitter
• Defocus
• 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

• 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 2.0.4	augly 1.0.0	imgaug 0.4.0	kornia 0.8.0	torchvision 0.20.1	Speedup (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

• LinkedIn
• Twitter
• Discord

Citing

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

@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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