https://github.com/albumentations-team/albumentationsx
Next-generation Albumentations: dual-licensed for open-source and commercial use
https://github.com/albumentations-team/albumentationsx
data-augmentation deep-learning deeplearning image-augmentation image-classification image-processing image-segmentations instance-segmentation maching-learning object-detection python
Last synced: 24 days ago
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Next-generation Albumentations: dual-licensed for open-source and commercial use
- Host: GitHub
- URL: https://github.com/albumentations-team/albumentationsx
- Owner: albumentations-team
- License: other
- Created: 2025-06-19T21:50:16.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2026-02-07T13:33:54.000Z (about 1 month ago)
- Last Synced: 2026-02-07T18:13:59.745Z (about 1 month ago)
- Topics: data-augmentation, deep-learning, deeplearning, image-augmentation, image-classification, image-processing, image-segmentations, instance-segmentation, maching-learning, object-detection, python
- Language: Python
- Homepage: https://albumentations.ai/
- Size: 1.42 MB
- Stars: 257
- Watchers: 1
- Forks: 23
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Maintainers: MAINTAINERS.md
- Cla: CLA.md
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README
# AlbumentationsX
[](https://badge.fury.io/py/albumentationsx)
[](https://pypi.org/project/albumentationsx/)
> 📣 **Stay updated!** [Subscribe to our newsletter](https://albumentations.ai/subscribe) for the latest releases, tutorials, and tips.
[](https://www.gnu.org/licenses/agpl-3.0)
[Docs](https://albumentations.ai/docs/) | [Discord](https://discord.gg/AKPrrDYNAt) | [Twitter](https://twitter.com/albumentations) | [LinkedIn](https://www.linkedin.com/company/100504475/)
**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.
## GitAds Sponsored
[](https://gitads.dev/v1/ad-track?source=albumentations-team/albumentationsx@github)
## 📢 Important: AlbumentationsX Licensing
AlbumentationsX offers dual licensing:
- **AGPL-3.0 License**: Free for open-source projects
- **Commercial License**: For proprietary/commercial use (contact for pricing)
### Quick Start
```bash
# Install AlbumentationsX with OpenCV
pip install albumentationsx[headless]
# Or if you already have OpenCV installed
pip install albumentationsx
```
```python
import albumentations as A
# Create your augmentation pipeline
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](https://albumentations.ai/pricing).
---
Here is an example of how you can apply some [pixel-level](#pixel-level-transforms) 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](#a-simple-example) for all data types - RGB/grayscale/multispectral images, masks, bounding boxes, and keypoints.
- **Rich Augmentation Library**: [70+ high-quality augmentations](https://albumentations.ai/docs/reference/supported-targets-by-transform/) to enhance your training data.
- **Fast**: Consistently benchmarked as the [fastest augmentation library](https://albumentations.ai/docs/benchmarks/image-benchmarks/) also shown [below section](#performance-comparison), with optimizations for production use.
- **Deep Learning Integration**: Works with [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and other frameworks. Part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/).
- **Created by Experts**: Built by [developers with deep experience in computer vision and machine learning competitions](#authors).
## Table of contents
- [AlbumentationsX](#albumentationsx)
- [Why AlbumentationsX](#why-albumentationsx)
- [Table of contents](#table-of-contents)
- [Authors](#authors)
- [Current Maintainer](#current-maintainer)
- [Emeritus Core Team Members](#emeritus-core-team-members)
- [Installation](#installation)
- [Documentation](#documentation)
- [A simple example](#a-simple-example)
- [List of augmentations](#list-of-augmentations)
- [Pixel-level transforms](#pixel-level-transforms)
- [Spatial-level transforms](#spatial-level-transforms)
- [A few more examples of **augmentations**](#a-few-more-examples-of-augmentations)
- [Semantic segmentation on the Inria dataset](#semantic-segmentation-on-the-inria-dataset)
- [Medical imaging](#medical-imaging)
- [Object detection and semantic segmentation on the Mapillary Vistas dataset](#object-detection-and-semantic-segmentation-on-the-mapillary-vistas-dataset)
- [Keypoints augmentation](#keypoints-augmentation)
- [Benchmarking results](#benchmark-results)
- [System Information](#system-information)
- [Benchmark Parameters](#benchmark-parameters)
- [Library Versions](#library-versions)
- [Performance Comparison](#performance-comparison)
- [🤝 Contribute](#-contribute)
- [📜 License](#-license)
- [📞 Contact](#-contact)
- [Citing](#citing)
## Authors
### Current Maintainer
[**Vladimir I. Iglovikov**](https://www.linkedin.com/in/iglovikov/) | [Kaggle Grandmaster](https://www.kaggle.com/iglovikov)
### Emeritus Core Team Members
[**Mikhail Druzhinin**](https://www.linkedin.com/in/mikhail-druzhinin-548229100/) | [Kaggle Expert](https://www.kaggle.com/dipetm)
[**Alex Parinov**](https://www.linkedin.com/in/alex-parinov/) | [Kaggle Master](https://www.kaggle.com/creafz)
[**Alexander Buslaev**](https://www.linkedin.com/in/al-buslaev/) | [Kaggle Master](https://www.kaggle.com/albuslaev)
[**Eugene Khvedchenya**](https://www.linkedin.com/in/cvtalks/) | [Kaggle Grandmaster](https://www.kaggle.com/bloodaxe)
## Installation
AlbumentationsX requires Python 3.10 or higher. To install the latest version from PyPI:
### Basic Installation
If you already have OpenCV installed (any variant), simply install AlbumentationsX:
```bash
pip install -U albumentationsx
```
### Installation with OpenCV
If you don't have OpenCV installed yet, choose the appropriate variant:
```bash
# 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.
Other installation options are described in the [documentation](https://albumentations.ai/docs/1-introduction/installation/).
## Documentation
The full documentation is available at **[https://albumentations.ai/docs/](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"]
```
AlbumentationsX collects anonymous usage statistics to improve the library. This can be disabled with `ALBUMENTATIONS_OFFLINE=1` or `ALBUMENTATIONS_NO_TELEMETRY=1`.
## 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](https://explore.albumentations.ai/transform/AdditiveNoise)
- [AdvancedBlur](https://explore.albumentations.ai/transform/AdvancedBlur)
- [AutoContrast](https://explore.albumentations.ai/transform/AutoContrast)
- [Blur](https://explore.albumentations.ai/transform/Blur)
- [CLAHE](https://explore.albumentations.ai/transform/CLAHE)
- [ChannelDropout](https://explore.albumentations.ai/transform/ChannelDropout)
- [ChannelShuffle](https://explore.albumentations.ai/transform/ChannelShuffle)
- [ChromaticAberration](https://explore.albumentations.ai/transform/ChromaticAberration)
- [ColorJitter](https://explore.albumentations.ai/transform/ColorJitter)
- [Defocus](https://explore.albumentations.ai/transform/Defocus)
- [Dithering](https://explore.albumentations.ai/transform/Dithering)
- [Downscale](https://explore.albumentations.ai/transform/Downscale)
- [Emboss](https://explore.albumentations.ai/transform/Emboss)
- [Equalize](https://explore.albumentations.ai/transform/Equalize)
- [FDA](https://explore.albumentations.ai/transform/FDA)
- [FancyPCA](https://explore.albumentations.ai/transform/FancyPCA)
- [FromFloat](https://explore.albumentations.ai/transform/FromFloat)
- [GaussNoise](https://explore.albumentations.ai/transform/GaussNoise)
- [GaussianBlur](https://explore.albumentations.ai/transform/GaussianBlur)
- [GlassBlur](https://explore.albumentations.ai/transform/GlassBlur)
- [HEStain](https://explore.albumentations.ai/transform/HEStain)
- [HistogramMatching](https://explore.albumentations.ai/transform/HistogramMatching)
- [HueSaturationValue](https://explore.albumentations.ai/transform/HueSaturationValue)
- [ISONoise](https://explore.albumentations.ai/transform/ISONoise)
- [Illumination](https://explore.albumentations.ai/transform/Illumination)
- [ImageCompression](https://explore.albumentations.ai/transform/ImageCompression)
- [InvertImg](https://explore.albumentations.ai/transform/InvertImg)
- [MedianBlur](https://explore.albumentations.ai/transform/MedianBlur)
- [MotionBlur](https://explore.albumentations.ai/transform/MotionBlur)
- [MultiplicativeNoise](https://explore.albumentations.ai/transform/MultiplicativeNoise)
- [Normalize](https://explore.albumentations.ai/transform/Normalize)
- [PixelDistributionAdaptation](https://explore.albumentations.ai/transform/PixelDistributionAdaptation)
- [PlanckianJitter](https://explore.albumentations.ai/transform/PlanckianJitter)
- [PlasmaBrightnessContrast](https://explore.albumentations.ai/transform/PlasmaBrightnessContrast)
- [PlasmaShadow](https://explore.albumentations.ai/transform/PlasmaShadow)
- [Posterize](https://explore.albumentations.ai/transform/Posterize)
- [RGBShift](https://explore.albumentations.ai/transform/RGBShift)
- [RandomBrightnessContrast](https://explore.albumentations.ai/transform/RandomBrightnessContrast)
- [RandomFog](https://explore.albumentations.ai/transform/RandomFog)
- [RandomGamma](https://explore.albumentations.ai/transform/RandomGamma)
- [RandomGravel](https://explore.albumentations.ai/transform/RandomGravel)
- [RandomRain](https://explore.albumentations.ai/transform/RandomRain)
- [RandomShadow](https://explore.albumentations.ai/transform/RandomShadow)
- [RandomSnow](https://explore.albumentations.ai/transform/RandomSnow)
- [RandomSunFlare](https://explore.albumentations.ai/transform/RandomSunFlare)
- [RandomToneCurve](https://explore.albumentations.ai/transform/RandomToneCurve)
- [RingingOvershoot](https://explore.albumentations.ai/transform/RingingOvershoot)
- [SaltAndPepper](https://explore.albumentations.ai/transform/SaltAndPepper)
- [Sharpen](https://explore.albumentations.ai/transform/Sharpen)
- [ShotNoise](https://explore.albumentations.ai/transform/ShotNoise)
- [Solarize](https://explore.albumentations.ai/transform/Solarize)
- [Spatter](https://explore.albumentations.ai/transform/Spatter)
- [Superpixels](https://explore.albumentations.ai/transform/Superpixels)
- [TextImage](https://explore.albumentations.ai/transform/TextImage)
- [ToFloat](https://explore.albumentations.ai/transform/ToFloat)
- [ToGray](https://explore.albumentations.ai/transform/ToGray)
- [ToRGB](https://explore.albumentations.ai/transform/ToRGB)
- [ToSepia](https://explore.albumentations.ai/transform/ToSepia)
- [UnsharpMask](https://explore.albumentations.ai/transform/UnsharpMask)
- [ZoomBlur](https://explore.albumentations.ai/transform/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 (HBB) | BBoxes (OBB) | Keypoints | Volume | Mask3D |
| ------------------------------------------------------------------------------------------------ | :---: | :--: | :----------: | :----------: | :-------: | :----: | :----: |
| [Affine](https://explore.albumentations.ai/transform/Affine) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [AtLeastOneBBoxRandomCrop](https://explore.albumentations.ai/transform/AtLeastOneBBoxRandomCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [BBoxSafeRandomCrop](https://explore.albumentations.ai/transform/BBoxSafeRandomCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [CenterCrop](https://explore.albumentations.ai/transform/CenterCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [CoarseDropout](https://explore.albumentations.ai/transform/CoarseDropout) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [ConstrainedCoarseDropout](https://explore.albumentations.ai/transform/ConstrainedCoarseDropout) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [Crop](https://explore.albumentations.ai/transform/Crop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [CropAndPad](https://explore.albumentations.ai/transform/CropAndPad) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [CropNonEmptyMaskIfExists](https://explore.albumentations.ai/transform/CropNonEmptyMaskIfExists) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [D4](https://explore.albumentations.ai/transform/D4) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [ElasticTransform](https://explore.albumentations.ai/transform/ElasticTransform) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Erasing](https://explore.albumentations.ai/transform/Erasing) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [FrequencyMasking](https://explore.albumentations.ai/transform/FrequencyMasking) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [GridDistortion](https://explore.albumentations.ai/transform/GridDistortion) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [GridDropout](https://explore.albumentations.ai/transform/GridDropout) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [GridElasticDeform](https://explore.albumentations.ai/transform/GridElasticDeform) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [HorizontalFlip](https://explore.albumentations.ai/transform/HorizontalFlip) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Lambda](https://explore.albumentations.ai/transform/Lambda) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [LongestMaxSize](https://explore.albumentations.ai/transform/LongestMaxSize) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [MaskDropout](https://explore.albumentations.ai/transform/MaskDropout) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [Morphological](https://explore.albumentations.ai/transform/Morphological) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [Mosaic](https://explore.albumentations.ai/transform/Mosaic) | ✓ | ✓ | ✓ | ✓ | ✓ | | |
| [NoOp](https://explore.albumentations.ai/transform/NoOp) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [OpticalDistortion](https://explore.albumentations.ai/transform/OpticalDistortion) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [OverlayElements](https://explore.albumentations.ai/transform/OverlayElements) | ✓ | ✓ | | | | | |
| [Pad](https://explore.albumentations.ai/transform/Pad) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [PadIfNeeded](https://explore.albumentations.ai/transform/PadIfNeeded) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Perspective](https://explore.albumentations.ai/transform/Perspective) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [PiecewiseAffine](https://explore.albumentations.ai/transform/PiecewiseAffine) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [PixelDropout](https://explore.albumentations.ai/transform/PixelDropout) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomCrop](https://explore.albumentations.ai/transform/RandomCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomCropFromBorders](https://explore.albumentations.ai/transform/RandomCropFromBorders) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomCropNearBBox](https://explore.albumentations.ai/transform/RandomCropNearBBox) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomGridShuffle](https://explore.albumentations.ai/transform/RandomGridShuffle) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [RandomResizedCrop](https://explore.albumentations.ai/transform/RandomResizedCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomRotate90](https://explore.albumentations.ai/transform/RandomRotate90) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomScale](https://explore.albumentations.ai/transform/RandomScale) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomSizedBBoxSafeCrop](https://explore.albumentations.ai/transform/RandomSizedBBoxSafeCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [RandomSizedCrop](https://explore.albumentations.ai/transform/RandomSizedCrop) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Resize](https://explore.albumentations.ai/transform/Resize) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Rotate](https://explore.albumentations.ai/transform/Rotate) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [SafeRotate](https://explore.albumentations.ai/transform/SafeRotate) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [ShiftScaleRotate](https://explore.albumentations.ai/transform/ShiftScaleRotate) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [SmallestMaxSize](https://explore.albumentations.ai/transform/SmallestMaxSize) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [SquareSymmetry](https://explore.albumentations.ai/transform/SquareSymmetry) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [ThinPlateSpline](https://explore.albumentations.ai/transform/ThinPlateSpline) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [TimeMasking](https://explore.albumentations.ai/transform/TimeMasking) | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ |
| [TimeReverse](https://explore.albumentations.ai/transform/TimeReverse) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [Transpose](https://explore.albumentations.ai/transform/Transpose) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [VerticalFlip](https://explore.albumentations.ai/transform/VerticalFlip) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| [XYMasking](https://explore.albumentations.ai/transform/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](https://explore.albumentations.ai/transform/CenterCrop3D) | ✓ | ✓ | ✓ |
| [CoarseDropout3D](https://explore.albumentations.ai/transform/CoarseDropout3D) | ✓ | ✓ | ✓ |
| [CubicSymmetry](https://explore.albumentations.ai/transform/CubicSymmetry) | ✓ | ✓ | ✓ |
| [GridShuffle3D](https://explore.albumentations.ai/transform/GridShuffle3D) | ✓ | ✓ | ✓ |
| [Pad3D](https://explore.albumentations.ai/transform/Pad3D) | ✓ | ✓ | ✓ |
| [PadIfNeeded3D](https://explore.albumentations.ai/transform/PadIfNeeded3D) | ✓ | ✓ | ✓ |
| [RandomCrop3D](https://explore.albumentations.ai/transform/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
- 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](CONTRIBUTING.md) to get started. A huge **Thank You** 🙏 to everyone who contributes!
[](https://github.com/albumentations-team/AlbumentationsX/graphs/contributors)
We look forward to your contributions to help make the AlbumentationsX ecosystem even better!
## 📜 License
AlbumentationsX offers two licensing options to suit different needs:
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/albumentations-team/AlbumentationsX/blob/main/LICENSE) file for full details.
- **AlbumentationsX Commercial License**: Designed for commercial use, this license allows for the seamless integration of AlbumentationsX into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please visit [our pricing page](https://albumentations.ai/pricing).
## 📞 Contact
For bug reports and feature requests related to AlbumentationsX, please visit [GitHub Issues](https://github.com/albumentations-team/AlbumentationsX/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.gg/AKPrrDYNAt), [Twitter](https://twitter.com/albumentations), and [LinkedIn](https://www.linkedin.com/company/albumentations/). We're here to help with all things AlbumentationsX!
## Citing
If you find this library useful for your research, please consider citing [Albumentations: Fast and Flexible Image Augmentations](https://www.mdpi.com/2078-2489/11/2/125):
```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}
}
```
---
## 📫 Stay Connected
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