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Original author(s) |
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Initial release | September 2016; 7 years ago (2016-09)[1] |
Repository | github |
Written in | |
Operating system |
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Available in | English |
Type | Library for machine learning and deep learning |
License | MIT[2] |
Website | 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.[3]
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.[4] 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.[5]
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.[6]
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["image"]