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Open Source Deep Learning Library

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Albumentations
Original author(s)	Alexander Buslaev Alex Parinov Vladimir I. Iglovikov Evegene Khvedchenya Mikhail Druzhinin
Initial release	September 2016; 8 years ago (2016-09)
Repository	github.com/albumentations-team/albumentations
Written in	Python
Operating system	Linux macOS Windows
Available in	English
Type	Library for machine learning and deep learning
License	MIT
Website	albumentations.ai

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Albumentations is an 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:

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

References

"First Commit". GitHub. 5 June 2018.
"MIT License". GitHub.
Alexander Buslaev; Vladimir Iglovikov; Alex Parinov; Eugene Khvedchenya; Alexandr A Kalinin (2020). "Albumentations: Fast and Flexible Image Augmentations". Information. 11 (2). MDPI: 125. arXiv:1809.06839. doi:10.3390/info11020125.
"Google Scholar - Albumentations: Fast and Flexible Image Augmentations". Google Scholar. Retrieved 2023-03-31.
"Albumentations GitHub Dependents". GitHub. Retrieved 2023-03-31.
"Albumentations - Who's Using?". Albumentations. Retrieved 2023-03-31.

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