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.
| Original author(s) |
|
|---|---|
| Initial release | September 2016[1] |
| Repository | github |
| Written in | |
| Operating system | |
| Available in | English |
| Type | Library for machine learning and deep learning |
| License | MIT[2] |
| Website | albumentations |
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]
Adoption
editAlbumentations 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]
Example
editThe 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"]
References
edit- ^ "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.