albumentationsx

Name	albumentationsx JSON
Version	2.0.12 JSON
	download
home_page	None
Summary	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.
upload_time	2025-10-08 12:50:55
maintainer	Vladimir Iglovikov
docs_url	None
author	Vladimir Iglovikov
requires_python	>=3.9
license	AlbumentationsX Dual License Notice =================================== AlbumentationsX is available under two different licensing options: 1. **AGPL-3.0 License** (this file) - For open source use - Free for open-source projects - Requires compliance with AGPL-3.0 terms - See full license text below 2. **Commercial License** - For proprietary/commercial use - No source code disclosure requirements - Full commercial support - Available for purchase at https://albumentations.ai/pricing For licensing questions, contact us at https://albumentations.ai/contact The full AGPL-3.0 license text follows: --- GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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keywords	2d augmentation 3d augmentation aerial photography anomaly detection artificial intelligence autonomous driving bounding boxes classification computer vision computer vision library data augmentation data preprocessing data science deep learning deep learning library depth estimation face recognition fast augmentation image augmentation image processing image transformation images instance segmentation keras keypoint detection keypoints machine learning machine learning tools masks medical imaging microscopy object counting object detection optimized performance panoptic segmentation pose estimation python library pytorch quality inspection real-time processing robotics vision satellite imagery semantic segmentation tensorflow volumes volumetric data volumetric masks
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            # AlbumentationsX

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[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.

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## 📢 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
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:
![parrot](https://habrastorage.org/webt/bd/ne/rv/bdnerv5ctkudmsaznhw4crsdfiw.jpeg)

## 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 3.9 or higher. To install the latest version from PyPI:

```bash
pip install -U albumentationsx
```

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 | 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

![inria](https://habrastorage.org/webt/su/wa/np/suwanpeo6ww7wpwtobtrzd_cg20.jpeg)

### Medical imaging

![medical](https://habrastorage.org/webt/1i/fi/wz/1ifiwzy0lxetc4nwjvss-71nkw0.jpeg)

### Object detection and semantic segmentation on the Mapillary Vistas dataset

![vistas](https://habrastorage.org/webt/rz/-h/3j/rz-h3jalbxic8o_fhucxysts4tc.jpeg)

### Keypoints augmentation

<img src="https://habrastorage.org/webt/e-/6k/z-/e-6kz-fugp2heak3jzns3bc-r8o.jpeg" width=100%>

## 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<br>2.0.8   | augly<br>1.0.0   | imgaug<br>0.4.0   | kornia<br>0.8.0   | torchvision<br>0.20.1   | Speedup<br>(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!

[![AlbumentationsX open-source contributors](https://contrib.rocks/image?repo=albumentations-team/AlbumentationsX)](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

Never miss updates, tutorials, and tips from the AlbumentationsX team! [Subscribe to our newsletter](https://albumentations.ai/subscribe).

<!-- GitAds-Verify: 99ZXCN5GQ9CQN3QEMO5H4RAOI8C5YTKV -->

            

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            {
    "_id": null,
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    "name": "albumentationsx",
    "maintainer": "Vladimir Iglovikov",
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": null,
    "keywords": "2D augmentation, 3D augmentation, aerial photography, anomaly detection, artificial intelligence, autonomous driving, bounding boxes, classification, computer vision, computer vision library, data augmentation, data preprocessing, data science, deep learning, deep learning library, depth estimation, face recognition, fast augmentation, image augmentation, image processing, image transformation, images, instance segmentation, keras, keypoint detection, keypoints, machine learning, machine learning tools, masks, medical imaging, microscopy, object counting, object detection, optimized performance, panoptic segmentation, pose estimation, python library, pytorch, quality inspection, real-time processing, robotics vision, satellite imagery, semantic segmentation, tensorflow, volumes, volumetric data, volumetric masks",
    "author": "Vladimir Iglovikov",
    "author_email": null,
    "download_url": "https://files.pythonhosted.org/packages/95/75/cde57e66ad0c249b8c872732f33bac4b05b7bd1b7a8c5921a96986e81f13/albumentationsx-2.0.12.tar.gz",
    "platform": null,
    "description": "# AlbumentationsX\n\n[![PyPI version](https://badge.fury.io/py/albumentationsx.svg)](https://badge.fury.io/py/albumentationsx)\n![CI](https://github.com/albumentations-team/AlbumentationsX/workflows/CI/badge.svg)\n[![PyPI Downloads](https://img.shields.io/pypi/dm/albumentationsx.svg?label=PyPI%20downloads)](https://pypi.org/project/albumentationsx/)\n\n> \ud83d\udce3 **Stay updated!** [Subscribe to our newsletter](https://albumentations.ai/subscribe) for the latest releases, tutorials, and tips.\n\n[![License: AGPL v3](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)\n\n[Docs](https://albumentations.ai/docs/) | [Discord](https://discord.gg/AKPrrDYNAt) | [Twitter](https://twitter.com/albumentations) | [LinkedIn](https://www.linkedin.com/company/100504475/)\n\n**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.\n\n## GitAds Sponsored\n\n[![Sponsored by GitAds](https://gitads.dev/v1/ad-serve?source=albumentations-team/albumentationsx@github)](https://gitads.dev/v1/ad-track?source=albumentations-team/albumentationsx@github)\n\n## \ud83d\udce2 Important: AlbumentationsX Licensing\n\nAlbumentationsX offers dual licensing:\n\n- **AGPL-3.0 License**: Free for open-source projects\n- **Commercial License**: For proprietary/commercial use (contact for pricing)\n\n### Quick Start\n\n```bash\n# Install AlbumentationsX\npip install albumentationsx\n```\n\n```python\nimport albumentations as A\n\n# Create your augmentation pipeline\ntransform = A.Compose([\n    A.RandomCrop(width=256, height=256),\n    A.HorizontalFlip(p=0.5),\n    A.RandomBrightnessContrast(p=0.2),\n])\n```\n\nFor commercial licensing inquiries, please visit [our pricing page](https://albumentations.ai/pricing).\n\n---\n\nHere is an example of how you can apply some [pixel-level](#pixel-level-transforms) augmentations to create new images from the original one:\n![parrot](https://habrastorage.org/webt/bd/ne/rv/bdnerv5ctkudmsaznhw4crsdfiw.jpeg)\n\n## Why AlbumentationsX\n\n- **Complete Computer Vision Support**: Works with all major CV tasks\n- **Simple, Unified API**: [One consistent interface](#a-simple-example) for all data types - RGB/grayscale/multispectral images, masks, bounding boxes, and keypoints.\n- **Rich Augmentation Library**: [70+ high-quality augmentations](https://albumentations.ai/docs/reference/supported-targets-by-transform/) to enhance your training data.\n- **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.\n- **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/).\n- **Created by Experts**: Built by [developers with deep experience in computer vision and machine learning competitions](#authors).\n\n## Table of contents\n\n- [AlbumentationsX](#albumentationsx)\n  - [Why AlbumentationsX](#why-albumentationsx)\n  - [Table of contents](#table-of-contents)\n  - [Authors](#authors)\n    - [Current Maintainer](#current-maintainer)\n    - [Emeritus Core Team Members](#emeritus-core-team-members)\n  - [Installation](#installation)\n  - [Documentation](#documentation)\n  - [A simple example](#a-simple-example)\n  - [List of augmentations](#list-of-augmentations)\n    - [Pixel-level transforms](#pixel-level-transforms)\n    - [Spatial-level transforms](#spatial-level-transforms)\n  - [A few more examples of **augmentations**](#a-few-more-examples-of-augmentations)\n    - [Semantic segmentation on the Inria dataset](#semantic-segmentation-on-the-inria-dataset)\n    - [Medical imaging](#medical-imaging)\n    - [Object detection and semantic segmentation on the Mapillary Vistas dataset](#object-detection-and-semantic-segmentation-on-the-mapillary-vistas-dataset)\n    - [Keypoints augmentation](#keypoints-augmentation)\n  - [Benchmarking results](#benchmark-results)\n    - [System Information](#system-information)\n    - [Benchmark Parameters](#benchmark-parameters)\n    - [Library Versions](#library-versions)\n  - [Performance Comparison](#performance-comparison)\n  - [\ud83e\udd1d Contribute](#-contribute)\n  - [\ud83d\udcdc License](#-license)\n  - [\ud83d\udcde Contact](#-contact)\n  - [Citing](#citing)\n\n## Authors\n\n### Current Maintainer\n\n[**Vladimir I. Iglovikov**](https://www.linkedin.com/in/iglovikov/) | [Kaggle Grandmaster](https://www.kaggle.com/iglovikov)\n\n### Emeritus Core Team Members\n\n[**Mikhail Druzhinin**](https://www.linkedin.com/in/mikhail-druzhinin-548229100/) | [Kaggle Expert](https://www.kaggle.com/dipetm)\n\n[**Alex Parinov**](https://www.linkedin.com/in/alex-parinov/) | [Kaggle Master](https://www.kaggle.com/creafz)\n\n[**Alexander Buslaev**](https://www.linkedin.com/in/al-buslaev/) | [Kaggle Master](https://www.kaggle.com/albuslaev)\n\n[**Eugene Khvedchenya**](https://www.linkedin.com/in/cvtalks/) | [Kaggle Grandmaster](https://www.kaggle.com/bloodaxe)\n\n## Installation\n\nAlbumentationsX requires Python 3.9 or higher. To install the latest version from PyPI:\n\n```bash\npip install -U albumentationsx\n```\n\nOther installation options are described in the [documentation](https://albumentations.ai/docs/1-introduction/installation/).\n\n## Documentation\n\nThe full documentation is available at **[https://albumentations.ai/docs/](https://albumentations.ai/docs/)**.\n\n## A simple example\n\n```python\nimport albumentations as A\nimport cv2\n\n# Declare an augmentation pipeline\ntransform = A.Compose([\n    A.RandomCrop(width=256, height=256),\n    A.HorizontalFlip(p=0.5),\n    A.RandomBrightnessContrast(p=0.2),\n])\n\n# Read an image with OpenCV and convert it to the RGB colorspace\nimage = cv2.imread(\"image.jpg\")\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n# Augment an image\ntransformed = transform(image=image)\ntransformed_image = transformed[\"image\"]\n```\n\nAlbumentationsX collects anonymous usage statistics to improve the library. This can be disabled with `ALBUMENTATIONS_OFFLINE=1` or `ALBUMENTATIONS_NO_TELEMETRY=1`.\n\n## List of augmentations\n\n### Pixel-level transforms\n\nPixel-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:\n\n- [AdditiveNoise](https://explore.albumentations.ai/transform/AdditiveNoise)\n- [AdvancedBlur](https://explore.albumentations.ai/transform/AdvancedBlur)\n- [AutoContrast](https://explore.albumentations.ai/transform/AutoContrast)\n- [Blur](https://explore.albumentations.ai/transform/Blur)\n- [CLAHE](https://explore.albumentations.ai/transform/CLAHE)\n- [ChannelDropout](https://explore.albumentations.ai/transform/ChannelDropout)\n- [ChannelShuffle](https://explore.albumentations.ai/transform/ChannelShuffle)\n- [ChromaticAberration](https://explore.albumentations.ai/transform/ChromaticAberration)\n- [ColorJitter](https://explore.albumentations.ai/transform/ColorJitter)\n- [Defocus](https://explore.albumentations.ai/transform/Defocus)\n- [Dithering](https://explore.albumentations.ai/transform/Dithering)\n- [Downscale](https://explore.albumentations.ai/transform/Downscale)\n- [Emboss](https://explore.albumentations.ai/transform/Emboss)\n- [Equalize](https://explore.albumentations.ai/transform/Equalize)\n- [FDA](https://explore.albumentations.ai/transform/FDA)\n- [FancyPCA](https://explore.albumentations.ai/transform/FancyPCA)\n- [FromFloat](https://explore.albumentations.ai/transform/FromFloat)\n- [GaussNoise](https://explore.albumentations.ai/transform/GaussNoise)\n- [GaussianBlur](https://explore.albumentations.ai/transform/GaussianBlur)\n- [GlassBlur](https://explore.albumentations.ai/transform/GlassBlur)\n- [HEStain](https://explore.albumentations.ai/transform/HEStain)\n- [HistogramMatching](https://explore.albumentations.ai/transform/HistogramMatching)\n- [HueSaturationValue](https://explore.albumentations.ai/transform/HueSaturationValue)\n- [ISONoise](https://explore.albumentations.ai/transform/ISONoise)\n- [Illumination](https://explore.albumentations.ai/transform/Illumination)\n- [ImageCompression](https://explore.albumentations.ai/transform/ImageCompression)\n- [InvertImg](https://explore.albumentations.ai/transform/InvertImg)\n- [MedianBlur](https://explore.albumentations.ai/transform/MedianBlur)\n- [MotionBlur](https://explore.albumentations.ai/transform/MotionBlur)\n- [MultiplicativeNoise](https://explore.albumentations.ai/transform/MultiplicativeNoise)\n- [Normalize](https://explore.albumentations.ai/transform/Normalize)\n- [PixelDistributionAdaptation](https://explore.albumentations.ai/transform/PixelDistributionAdaptation)\n- [PlanckianJitter](https://explore.albumentations.ai/transform/PlanckianJitter)\n- [PlasmaBrightnessContrast](https://explore.albumentations.ai/transform/PlasmaBrightnessContrast)\n- [PlasmaShadow](https://explore.albumentations.ai/transform/PlasmaShadow)\n- [Posterize](https://explore.albumentations.ai/transform/Posterize)\n- [RGBShift](https://explore.albumentations.ai/transform/RGBShift)\n- [RandomBrightnessContrast](https://explore.albumentations.ai/transform/RandomBrightnessContrast)\n- [RandomFog](https://explore.albumentations.ai/transform/RandomFog)\n- [RandomGamma](https://explore.albumentations.ai/transform/RandomGamma)\n- [RandomGravel](https://explore.albumentations.ai/transform/RandomGravel)\n- [RandomRain](https://explore.albumentations.ai/transform/RandomRain)\n- [RandomShadow](https://explore.albumentations.ai/transform/RandomShadow)\n- [RandomSnow](https://explore.albumentations.ai/transform/RandomSnow)\n- [RandomSunFlare](https://explore.albumentations.ai/transform/RandomSunFlare)\n- [RandomToneCurve](https://explore.albumentations.ai/transform/RandomToneCurve)\n- [RingingOvershoot](https://explore.albumentations.ai/transform/RingingOvershoot)\n- [SaltAndPepper](https://explore.albumentations.ai/transform/SaltAndPepper)\n- [Sharpen](https://explore.albumentations.ai/transform/Sharpen)\n- [ShotNoise](https://explore.albumentations.ai/transform/ShotNoise)\n- [Solarize](https://explore.albumentations.ai/transform/Solarize)\n- [Spatter](https://explore.albumentations.ai/transform/Spatter)\n- [Superpixels](https://explore.albumentations.ai/transform/Superpixels)\n- [TextImage](https://explore.albumentations.ai/transform/TextImage)\n- [ToFloat](https://explore.albumentations.ai/transform/ToFloat)\n- [ToGray](https://explore.albumentations.ai/transform/ToGray)\n- [ToRGB](https://explore.albumentations.ai/transform/ToRGB)\n- [ToSepia](https://explore.albumentations.ai/transform/ToSepia)\n- [UnsharpMask](https://explore.albumentations.ai/transform/UnsharpMask)\n- [ZoomBlur](https://explore.albumentations.ai/transform/ZoomBlur)\n\n### Spatial-level transforms\n\nSpatial-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:\n\n- 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)\n- Mask3D: Binary or multi-class 3D mask of shape (D, H, W) where each slice represents segmentation for the corresponding volume slice\n\n| Transform                                                                                        | Image | Mask | BBoxes | Keypoints | Volume | Mask3D |\n| ------------------------------------------------------------------------------------------------ | :---: | :--: | :----: | :-------: | :----: | :----: |\n| [Affine](https://explore.albumentations.ai/transform/Affine)                                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [AtLeastOneBBoxRandomCrop](https://explore.albumentations.ai/transform/AtLeastOneBBoxRandomCrop) | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [BBoxSafeRandomCrop](https://explore.albumentations.ai/transform/BBoxSafeRandomCrop)             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [CenterCrop](https://explore.albumentations.ai/transform/CenterCrop)                             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [CoarseDropout](https://explore.albumentations.ai/transform/CoarseDropout)                       | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [ConstrainedCoarseDropout](https://explore.albumentations.ai/transform/ConstrainedCoarseDropout) | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Crop](https://explore.albumentations.ai/transform/Crop)                                         | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [CropAndPad](https://explore.albumentations.ai/transform/CropAndPad)                             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [CropNonEmptyMaskIfExists](https://explore.albumentations.ai/transform/CropNonEmptyMaskIfExists) | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [D4](https://explore.albumentations.ai/transform/D4)                                             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [ElasticTransform](https://explore.albumentations.ai/transform/ElasticTransform)                 | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Erasing](https://explore.albumentations.ai/transform/Erasing)                                   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [FrequencyMasking](https://explore.albumentations.ai/transform/FrequencyMasking)                 | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [GridDistortion](https://explore.albumentations.ai/transform/GridDistortion)                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [GridDropout](https://explore.albumentations.ai/transform/GridDropout)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [GridElasticDeform](https://explore.albumentations.ai/transform/GridElasticDeform)               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [HorizontalFlip](https://explore.albumentations.ai/transform/HorizontalFlip)                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Lambda](https://explore.albumentations.ai/transform/Lambda)                                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [LongestMaxSize](https://explore.albumentations.ai/transform/LongestMaxSize)                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [MaskDropout](https://explore.albumentations.ai/transform/MaskDropout)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Morphological](https://explore.albumentations.ai/transform/Morphological)                       | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Mosaic](https://explore.albumentations.ai/transform/Mosaic)                                     | \u2713     | \u2713    | \u2713      | \u2713         |        |        |\n| [NoOp](https://explore.albumentations.ai/transform/NoOp)                                         | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [OpticalDistortion](https://explore.albumentations.ai/transform/OpticalDistortion)               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [OverlayElements](https://explore.albumentations.ai/transform/OverlayElements)                   | \u2713     | \u2713    |        |           |        |        |\n| [Pad](https://explore.albumentations.ai/transform/Pad)                                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [PadIfNeeded](https://explore.albumentations.ai/transform/PadIfNeeded)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Perspective](https://explore.albumentations.ai/transform/Perspective)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [PiecewiseAffine](https://explore.albumentations.ai/transform/PiecewiseAffine)                   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [PixelDropout](https://explore.albumentations.ai/transform/PixelDropout)                         | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomCrop](https://explore.albumentations.ai/transform/RandomCrop)                             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomCropFromBorders](https://explore.albumentations.ai/transform/RandomCropFromBorders)       | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomCropNearBBox](https://explore.albumentations.ai/transform/RandomCropNearBBox)             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomGridShuffle](https://explore.albumentations.ai/transform/RandomGridShuffle)               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomResizedCrop](https://explore.albumentations.ai/transform/RandomResizedCrop)               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomRotate90](https://explore.albumentations.ai/transform/RandomRotate90)                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomScale](https://explore.albumentations.ai/transform/RandomScale)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomSizedBBoxSafeCrop](https://explore.albumentations.ai/transform/RandomSizedBBoxSafeCrop)   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [RandomSizedCrop](https://explore.albumentations.ai/transform/RandomSizedCrop)                   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Resize](https://explore.albumentations.ai/transform/Resize)                                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Rotate](https://explore.albumentations.ai/transform/Rotate)                                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [SafeRotate](https://explore.albumentations.ai/transform/SafeRotate)                             | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [ShiftScaleRotate](https://explore.albumentations.ai/transform/ShiftScaleRotate)                 | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [SmallestMaxSize](https://explore.albumentations.ai/transform/SmallestMaxSize)                   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [SquareSymmetry](https://explore.albumentations.ai/transform/SquareSymmetry)                     | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [ThinPlateSpline](https://explore.albumentations.ai/transform/ThinPlateSpline)                   | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [TimeMasking](https://explore.albumentations.ai/transform/TimeMasking)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [TimeReverse](https://explore.albumentations.ai/transform/TimeReverse)                           | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [Transpose](https://explore.albumentations.ai/transform/Transpose)                               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [VerticalFlip](https://explore.albumentations.ai/transform/VerticalFlip)                         | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n| [XYMasking](https://explore.albumentations.ai/transform/XYMasking)                               | \u2713     | \u2713    | \u2713      | \u2713         | \u2713      | \u2713      |\n\n### 3D transforms\n\n3D transforms operate on volumetric data and can modify both the input volume and associated 3D mask.\n\nWhere:\n\n- 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)\n- Mask3D: Binary or multi-class 3D mask of shape (D, H, W) where each slice represents segmentation for the corresponding volume slice\n\n| Transform                                                                      | Volume | Mask3D | Keypoints |\n| ------------------------------------------------------------------------------ | :----: | :----: | :-------: |\n| [CenterCrop3D](https://explore.albumentations.ai/transform/CenterCrop3D)       | \u2713      | \u2713      | \u2713         |\n| [CoarseDropout3D](https://explore.albumentations.ai/transform/CoarseDropout3D) | \u2713      | \u2713      | \u2713         |\n| [CubicSymmetry](https://explore.albumentations.ai/transform/CubicSymmetry)     | \u2713      | \u2713      | \u2713         |\n| [GridShuffle3D](https://explore.albumentations.ai/transform/GridShuffle3D)     | \u2713      | \u2713      | \u2713         |\n| [Pad3D](https://explore.albumentations.ai/transform/Pad3D)                     | \u2713      | \u2713      | \u2713         |\n| [PadIfNeeded3D](https://explore.albumentations.ai/transform/PadIfNeeded3D)     | \u2713      | \u2713      | \u2713         |\n| [RandomCrop3D](https://explore.albumentations.ai/transform/RandomCrop3D)       | \u2713      | \u2713      | \u2713         |\n\n## A few more examples of **augmentations**\n\n### Semantic segmentation on the Inria dataset\n\n![inria](https://habrastorage.org/webt/su/wa/np/suwanpeo6ww7wpwtobtrzd_cg20.jpeg)\n\n### Medical imaging\n\n![medical](https://habrastorage.org/webt/1i/fi/wz/1ifiwzy0lxetc4nwjvss-71nkw0.jpeg)\n\n### Object detection and semantic segmentation on the Mapillary Vistas dataset\n\n![vistas](https://habrastorage.org/webt/rz/-h/3j/rz-h3jalbxic8o_fhucxysts4tc.jpeg)\n\n### Keypoints augmentation\n\n<img src=\"https://habrastorage.org/webt/e-/6k/z-/e-6kz-fugp2heak3jzns3bc-r8o.jpeg\" width=100%>\n\n## Benchmark Results\n\n### Image Benchmark Results\n\n### System Information\n\n- Platform: macOS-15.1-arm64-arm-64bit\n- Processor: arm\n- CPU Count: 16\n- Python Version: 3.12.8\n\n### Benchmark Parameters\n\n- Number of images: 2000\n- Runs per transform: 5\n- Max warmup iterations: 1000\n\n### Library Versions\n\n- albumentationsx: 2.0.8\n- augly: 1.0.0\n- imgaug: 0.4.0\n- kornia: 0.8.0\n- torchvision: 0.20.1\n\n## Performance Comparison\n\nNumber shows how many uint8 images per second can be processed on one CPU thread. Larger is better.\nThe Speedup column shows how many times faster AlbumentationsX is compared to the fastest other\nlibrary for each transform.\n\n| Transform            | albumentationsx<br>2.0.8   | augly<br>1.0.0   | imgaug<br>0.4.0   | kornia<br>0.8.0   | torchvision<br>0.20.1   | Speedup<br>(AlbX/fastest other)   |\n|:---------------------|:--------------------------|:-----------------|:------------------|:------------------|:------------------------|:---------------------------------|\n| Affine               | **1445 \u00b1 9**              | -                | 1328 \u00b1 16         | 248 \u00b1 6           | 188 \u00b1 2                 | 1.09x                            |\n| AutoContrast         | **1657 \u00b1 13**             | -                | -                 | 541 \u00b1 8           | 344 \u00b1 1                 | 3.06x                            |\n| Blur                 | **7657 \u00b1 114**            | 386 \u00b1 4          | 5381 \u00b1 125        | 265 \u00b1 11          | -                       | 1.42x                            |\n| Brightness           | **11985 \u00b1 455**           | 2108 \u00b1 32        | 1076 \u00b1 32         | 1127 \u00b1 27         | 854 \u00b1 13                | 5.68x                            |\n| CLAHE                | **647 \u00b1 4**               | -                | 555 \u00b1 14          | 165 \u00b1 3           | -                       | 1.17x                            |\n| CenterCrop128        | **119293 \u00b1 2164**         | -                | -                 | -                 | -                       | N/A                              |\n| ChannelDropout       | **11534 \u00b1 306**           | -                | -                 | 2283 \u00b1 24         | -                       | 5.05x                            |\n| ChannelShuffle       | **6772 \u00b1 109**            | -                | 1252 \u00b1 26         | 1328 \u00b1 44         | 4417 \u00b1 234              | 1.53x                            |\n| CoarseDropout        | **18962 \u00b1 1346**          | -                | 1190 \u00b1 22         | -                 | -                       | 15.93x                           |\n| ColorJitter          | **1020 \u00b1 91**             | 418 \u00b1 5          | -                 | 104 \u00b1 4           | 87 \u00b1 1                  | 2.44x                            |\n| Contrast             | **12394 \u00b1 363**           | 1379 \u00b1 25        | 717 \u00b1 5           | 1109 \u00b1 41         | 602 \u00b1 13                | 8.99x                            |\n| CornerIllumination   | **484 \u00b1 7**               | -                | -                 | 452 \u00b1 3           | -                       | 1.07x                            |\n| Elastic              | 374 \u00b1 2                   | -                | **395 \u00b1 14**      | 1 \u00b1 0             | 3 \u00b1 0                   | 0.95x                            |\n| Equalize             | **1236 \u00b1 21**             | -                | 814 \u00b1 11          | 306 \u00b1 1           | 795 \u00b1 3                 | 1.52x                            |\n| Erasing              | **27451 \u00b1 2794**          | -                | -                 | 1210 \u00b1 27         | 3577 \u00b1 49               | 7.67x                            |\n| GaussianBlur         | **2350 \u00b1 118**            | 387 \u00b1 4          | 1460 \u00b1 23         | 254 \u00b1 5           | 127 \u00b1 4                 | 1.61x                            |\n| GaussianIllumination | **720 \u00b1 7**               | -                | -                 | 436 \u00b1 13          | -                       | 1.65x                            |\n| GaussianNoise        | **315 \u00b1 4**               | -                | 263 \u00b1 9           | 125 \u00b1 1           | -                       | 1.20x                            |\n| Grayscale            | **32284 \u00b1 1130**          | 6088 \u00b1 107       | 3100 \u00b1 24         | 1201 \u00b1 52         | 2600 \u00b1 23               | 5.30x                            |\n| HSV                  | **1197 \u00b1 23**             | -                | -                 | -                 | -                       | N/A                              |\n| HorizontalFlip       | **14460 \u00b1 368**           | 8808 \u00b1 1012      | 9599 \u00b1 495        | 1297 \u00b1 13         | 2486 \u00b1 107              | 1.51x                            |\n| Hue                  | **1944 \u00b1 64**             | -                | -                 | 150 \u00b1 1           | -                       | 12.98x                           |\n| Invert               | **27665 \u00b1 3803**          | -                | 3682 \u00b1 79         | 2881 \u00b1 43         | 4244 \u00b1 30               | 6.52x                            |\n| JpegCompression      | **1321 \u00b1 33**             | 1202 \u00b1 19        | 687 \u00b1 26          | 120 \u00b1 1           | 889 \u00b1 7                 | 1.10x                            |\n| LinearIllumination   | 479 \u00b1 5                   | -                | -                 | **708 \u00b1 6**       | -                       | 0.68x                            |\n| MedianBlur           | **1229 \u00b1 9**              | -                | 1152 \u00b1 14         | 6 \u00b1 0             | -                       | 1.07x                            |\n| MotionBlur           | **3521 \u00b1 25**             | -                | 928 \u00b1 37          | 159 \u00b1 1           | -                       | 3.79x                            |\n| Normalize            | **1819 \u00b1 49**             | -                | -                 | 1251 \u00b1 14         | 1018 \u00b1 7                | 1.45x                            |\n| OpticalDistortion    | **661 \u00b1 7**               | -                | -                 | 174 \u00b1 0           | -                       | 3.80x                            |\n| Pad                  | **48589 \u00b1 2059**          | -                | -                 | -                 | 4889 \u00b1 183              | 9.94x                            |\n| Perspective          | **1206 \u00b1 3**              | -                | 908 \u00b1 8           | 154 \u00b1 3           | 147 \u00b1 5                 | 1.33x                            |\n| PlankianJitter       | **3221 \u00b1 63**             | -                | -                 | 2150 \u00b1 52         | -                       | 1.50x                            |\n| PlasmaBrightness     | **168 \u00b1 2**               | -                | -                 | 85 \u00b1 1            | -                       | 1.98x                            |\n| PlasmaContrast       | **145 \u00b1 3**               | -                | -                 | 84 \u00b1 0            | -                       | 1.71x                            |\n| PlasmaShadow         | 183 \u00b1 5                   | -                | -                 | **216 \u00b1 5**       | -                       | 0.85x                            |\n| Posterize            | **12979 \u00b1 1121**          | -                | 3111 \u00b1 95         | 836 \u00b1 30          | 4247 \u00b1 26               | 3.06x                            |\n| RGBShift             | **3391 \u00b1 104**            | -                | -                 | 896 \u00b1 9           | -                       | 3.79x                            |\n| Rain                 | **2043 \u00b1 115**            | -                | -                 | 1493 \u00b1 9          | -                       | 1.37x                            |\n| RandomCrop128        | **111859 \u00b1 1374**         | 45395 \u00b1 934      | 21408 \u00b1 622       | 2946 \u00b1 42         | 31450 \u00b1 249             | 2.46x                            |\n| RandomGamma          | **12444 \u00b1 753**           | -                | 3504 \u00b1 72         | 230 \u00b1 3           | -                       | 3.55x                            |\n| RandomResizedCrop    | **4347 \u00b1 37**             | -                | -                 | 661 \u00b1 16          | 837 \u00b1 37                | 5.19x                            |\n| Resize               | **3532 \u00b1 67**             | 1083 \u00b1 21        | 2995 \u00b1 70         | 645 \u00b1 13          | 260 \u00b1 9                 | 1.18x                            |\n| Rotate               | **2912 \u00b1 68**             | 1739 \u00b1 105       | 2574 \u00b1 10         | 256 \u00b1 2           | 258 \u00b1 4                 | 1.13x                            |\n| SaltAndPepper        | **629 \u00b1 6**               | -                | -                 | 480 \u00b1 12          | -                       | 1.31x                            |\n| Saturation           | **1596 \u00b1 24**             | -                | 495 \u00b1 3           | 155 \u00b1 2           | -                       | 3.22x                            |\n| Sharpen              | **2346 \u00b1 10**             | -                | 1101 \u00b1 30         | 201 \u00b1 2           | 220 \u00b1 3                 | 2.13x                            |\n| Shear                | **1299 \u00b1 11**             | -                | 1244 \u00b1 14         | 261 \u00b1 1           | -                       | 1.04x                            |\n| Snow                 | **611 \u00b1 9**               | -                | -                 | 143 \u00b1 1           | -                       | 4.28x                            |\n| Solarize             | **11756 \u00b1 481**           | -                | 3843 \u00b1 80         | 263 \u00b1 6           | 1032 \u00b1 14               | 3.06x                            |\n| ThinPlateSpline      | **82 \u00b1 1**                | -                | -                 | 58 \u00b1 0            | -                       | 1.41x                            |\n| VerticalFlip         | **32386 \u00b1 936**           | 16830 \u00b1 1653     | 19935 \u00b1 1708      | 2872 \u00b1 37         | 4696 \u00b1 161              | 1.62x                            |\n\n## \ud83e\udd1d Contribute\n\nWe 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** \ud83d\ude4f to everyone who contributes!\n\n[![AlbumentationsX open-source contributors](https://contrib.rocks/image?repo=albumentations-team/AlbumentationsX)](https://github.com/albumentations-team/AlbumentationsX/graphs/contributors)\n\nWe look forward to your contributions to help make the AlbumentationsX ecosystem even better!\n\n## \ud83d\udcdc License\n\nAlbumentationsX offers two licensing options to suit different needs:\n\n- **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.\n- **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).\n\n## \ud83d\udcde Contact\n\nFor 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!\n\n## Citing\n\nIf 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):\n\n```bibtex\n@Article{info11020125,\n    AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.},\n    TITLE = {Albumentations: Fast and Flexible Image Augmentations},\n    JOURNAL = {Information},\n    VOLUME = {11},\n    YEAR = {2020},\n    NUMBER = {2},\n    ARTICLE-NUMBER = {125},\n    URL = {https://www.mdpi.com/2078-2489/11/2/125},\n    ISSN = {2078-2489},\n    DOI = {10.3390/info11020125}\n}\n```\n\n---\n\n## \ud83d\udceb Stay Connected\n\nNever miss updates, tutorials, and tips from the AlbumentationsX team! [Subscribe to our newsletter](https://albumentations.ai/subscribe).\n\n<!-- GitAds-Verify: 99ZXCN5GQ9CQN3QEMO5H4RAOI8C5YTKV -->\n",
    "bugtrack_url": null,
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    "summary": "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.",
    "version": "2.0.12",
    "project_urls": {
        "Homepage": "https://albumentations.ai"
    },
    "split_keywords": [
        "2d augmentation",
        " 3d augmentation",
        " aerial photography",
        " anomaly detection",
        " artificial intelligence",
        " autonomous driving",
        " bounding boxes",
        " classification",
        " computer vision",
        " computer vision library",
        " data augmentation",
        " data preprocessing",
        " data science",
        " deep learning",
        " deep learning library",
        " depth estimation",
        " face recognition",
        " fast augmentation",
        " image augmentation",
        " image processing",
        " image transformation",
        " images",
        " instance segmentation",
        " keras",
        " keypoint detection",
        " keypoints",
        " machine learning",
        " machine learning tools",
        " masks",
        " medical imaging",
        " microscopy",
        " object counting",
        " object detection",
        " optimized performance",
        " panoptic segmentation",
        " pose estimation",
        " python library",
        " pytorch",
        " quality inspection",
        " real-time processing",
        " robotics vision",
        " satellite imagery",
        " semantic segmentation",
        " tensorflow",
        " volumes",
        " volumetric data",
        " volumetric masks"
    ],
    "urls": [
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