Albumentations

Albumentations is a powerful open-source image augmentation library created in June 2018 by a group of researchers and engineers, including Alexander Buslaev, Vladimir Iglovikov, and Alex Parinov. The library was designed to provide a flexible and efficient framework for data augmentation in computer vision tasks.

Data augmentation is a technique that involves artificially expanding the size of a dataset by creating new images through various transformations such as rotation, scaling, flipping, and color adjustments. This process helps improve the performance of machine learning models by providing a more diverse set of training examples.

Built on top of OpenCV, a widely used computer vision library, Albumentations provides high-performance implementations of various image processing functions. It also offers a rich set of image transformation functions and a simple API for combining them, allowing users to create custom augmentation pipelines tailored to their specific needs.

Adoption
Albumentations has gained significant popularity and recognition in the computer vision and deep learning community since its introduction in 2018. The library was designed to provide a flexible and efficient framework for data augmentation in computer vision tasks, and has been widely adopted in academic research, open-source projects, and machine learning competitions.

The library's research paper, "Albumentations: Fast and Flexible Image Augmentations," has received over 1000 citations, highlighting its importance and impact in the field of computer vision. The library has also been widely adopted in computer vision and deep learning projects, with over 12,000 packages depending on it as listed on its GitHub dependents page.

In addition, Albumentations has been used in many winning solutions for computer vision competitions, including the DeepFake Detection challenge at Kaggle with a prize of 1 million dollars.

Example
The following program shows the functionality of the library with a simple example: