Welcome to Albumentations documentation¶
Albumentations is a fast and flexible image augmentation library. The library is widely used in industry, deep learning research, machine learning competitions, and open source projects. Albumentations is written in Python, and it is licensed under the MIT license. The source code is available at https://github.com/albumentations-team/albumentations.
If you are new to image augmentation, start with articles in the "Introduction to image augmentation" section. They describe what image augmentation is, how it can boost deep neural networks' performance, and why you should use Albumentations.
Articles in the "Getting started with Albumentations" section show how you can use the library for different computer vision tasks: image classification, semantic segmentation, instance segmentation, and object detection, keypoint detection.
The "Examples" section contains Jupyter Notebooks that demonstrate how to use various features of Albumentations. Each notebook includes a link to Google Colab, where you can run the code by yourself.
"API Reference" contains the description of Albumentations' methods and classes.
Introduction to image augmentation¶
- What is image augmentation and how it can improve the performance of deep neural networks
- Why you need a dedicated library for image augmentation
- Why Albumentations
Getting started with Albumentations¶
- Image augmentation for classification
- Mask augmentation for segmentation
- Bounding boxes augmentation for object detection
- Keypoints augmentation
- Simultaneous augmentation of multiple targets: masks, bounding boxes, keypoints
- A list of transforms and their supported targets
- Setting probabilities for transforms in an augmentation pipeline
- Defining a simple augmentation pipeline for image augmentation
- Working with non-8-bit images
- Using Albumentations to augment bounding boxes for object detection tasks
- How to use Albumentations for detection tasks if you need to keep all bounding boxes
- Using Albumentations for a semantic segmentation task
- Using Albumentations to augment keypoints
- Applying the same augmentation with the same parameters to multiple images, masks, bounding boxes, or keypoints
- Weather augmentations in Albumentations
- Example of applying XYMasking transform
- Migrating from torchvision to Albumentations
- Debugging an augmentation pipeline with ReplayCompose
- How to save and load parameters of an augmentation pipeline
- Showcase. Cool augmentation examples on diverse set of images from various real-world tasks.
Examples of how to use Albumentations with different deep learning frameworks¶
- TensorFlow 2
- Frequently Asked Questions
- AutoAlbument - AutoML for image augmentation
- Albumentations Experimental - experimental features for Albumentations
- Release notes