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⚠️ Important Notice: Albumentations is No Longer MaintainedThis repository is no longer actively maintained.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 AlbumentationsAll development has moved to AlbumentationsX, the next-generation successor to Albumentations.
Your Options Moving Forward 1. Continue Using Albumentations (MIT License)Note: AlbumentationsX uses dual licensing (AGPL-3.0 / Commercial). The AGPL license has strict copyleft requirements - see details below.
Best for: Projects that work fine with the current version and don't need updates
2. Upgrade to AlbumentationsX (Dual Licensed)
pip install albumentationsxBest 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.
# Uninstall original pip uninstall albumentations # Install AlbumentationsX pip install albumentationsx
That's it! Your existing code continues to work without any changes:
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),
])
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:
Vladimir I. Iglovikov | Kaggle Grandmaster
Emeritus Core Team MembersMikhail Druzhinin | Kaggle Expert
Alexander Buslaev | Kaggle Master
Eugene Khvedchenya | Kaggle Grandmaster
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.
The full documentation is available at https://albumentations.ai/docs/.
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"] 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 segmentationIf 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 actionCheck 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 AlbumentationsPixel-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 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:
3D transforms operate on volumetric data and can modify both the input volume and associated 3D mask.
Where:
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 albumentationsTo create a pull request to the repository, follow the documentation at CONTRIBUTING.md
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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