Albumentations

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Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

Here is an example of how you can apply some pixel-level augmentations from Albumentations to create new images from the original one: parrot

Why Albumentations

Complete Computer Vision Support: Works with all major CV tasks including classification, segmentation (semantic & instance), object detection, and pose estimation.
Simple, Unified API: One consistent interface for all data types - RGB/grayscale/multispectral images, masks, bounding boxes, and keypoints.
Rich Augmentation Library: 70+ high-quality augmentations to enhance your training data.
Fast: Consistently benchmarked as the fastest augmentation library also shown below section, with optimizations for production use.
Deep Learning Integration: Works with PyTorch, TensorFlow, and other frameworks. Part of the PyTorch ecosystem.
Created by Experts: Built by developers with deep experience in computer vision and machine learning competitions.

Community-Driven Project, Supported By

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Albumentations

Authors

Current Maintainer

Vladimir I. Iglovikov | Kaggle Grandmaster

Emeritus Core Team Members

Mikhail Druzhinin | Kaggle Expert

Alex Parinov | Kaggle Master

Alexander Buslaev | Kaggle Master

Eugene Khvedchenya | Kaggle Grandmaster

Installation

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

pip install -U albumentations

Other installation options are described in the documentation.

Documentation

The full documentation is available at https://albumentations.ai/docs/.

A simple example

import albumentations as A
import cv2

# Declare an augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Augment an image
transformed = transform(image=image)
transformed_image = transformed["image"]

Getting started

I am new to image augmentation

Please start with the introduction articles about why image augmentation is important and how it helps to build better models.

I want to use Albumentations for the specific task such as classification or segmentation

If you want to use Albumentations for a specific task such as classification, segmentation, or object detection, refer to the set of articles that has an in-depth description of this task. We also have a list of examples on applying Albumentations for different use cases.

I want to know how to use Albumentations with deep learning frameworks

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

I want to explore augmentations and see Albumentations in action

Check the online demo of the library. With it, you can apply augmentations to different images and see the result. Also, we have a list of all available augmentations and their targets.

Who is using Albumentations

<a href="https://www.apple.com/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/apple.jpeg" width="100"/></a> <a href="https://research.google/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/google.png" width="100"/></a> <a href="https://opensource.fb.com/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/meta_research.png" width="100"/></a> <a href="https://www.nvidia.com/en-us/research/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/nvidia_research.jpeg" width="100"/></a> <a href="https://www.amazon.science/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/amazon_science.png" width="100"/></a> <a href="https://opensource.microsoft.com/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/microsoft.png" width="100"/></a> <a href="https://engineering.salesforce.com/open-source/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/salesforce_open_source.png" width="100"/></a> <a href="https://stability.ai/" target="_blank"><img src="https://www.albumentations.ai/assets/industry/stability.png" width="100"/></a> <a href="https://www.ibm.com/opensource/" target="_blank"><img src="https://www.albumentations.a

albumentations

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