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.
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AlbumentationsX is a drop-in replacement for Albumentations - a Python library for image augmentation. It maintains 100% API compatibility while providing improved performance, bug fixes, and new features. 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.
AlbumentationsX is a fork of the original Albumentations library with dual licensing:
The original Albumentations library is MIT licensed, supports Python 3.9-3.13, and can be used freely for all purposes including commercial applications without any restrictions or licensing fees. If its current functionality meets your needs, that's great!
However, please note that the original library is no longer actively maintained. All new bug fixes, performance improvements, and features will be exclusively developed in AlbumentationsX.
AlbumentationsX is designed as a 100% compatible drop-in replacement for the original Albumentations library. This means:
import albumentations as ASimple migration process:
# Uninstall original
pip uninstall albumentations
# Install AlbumentationsX
pip install albumentationsx
That's it! Your code continues to work exactly as before:
# No changes needed - same import!
import albumentations as A
# All your existing code works unchanged
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 (coming soon).
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
Mikhail Druzhinin | Kaggle Expert
Alexander Buslaev | Kaggle Master
Eugene Khvedchenya | Kaggle Grandmaster
AlbumentationsX requires Python 3.9 or higher. To install the latest version from PyPI:
pip install -U albumentationsx
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"]
AlbumentationsX checks for updates on import and collects anonymous usage statistics to improve the library. Both features can be disabled with ALBUMENTATIONS_OFFLINE=1, or individually with NO_ALBUMENTATIONS_UPDATE=1 (version check) and ALBUMENTATIONS_NO_TELEMETRY=1 (telemetry).
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 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:
| 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 operate on volumetric data and can modify both the input volume and associated 3D mask.
Where:
| Transform | Volume | Mask3D | Keypoints |
|---|---|---|---|
| CenterCrop3D | ✓ | ✓ | ✓ |
| CoarseDropout3D | ✓ | ✓ | ✓ |
| CubicSymmetry | ✓ | ✓ | ✓ |
| Pad3D | ✓ | ✓ | ✓ |
| PadIfNeeded3D | ✓ | ✓ | ✓ |
| RandomCrop3D | ✓ | ✓ | ✓ |
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 | 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 |
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!
AlbumentationsX offers two licensing options to suit different needs:
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!
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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FAQs
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.
We found that albumentationsx demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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