1 research outputs found
Albumentations: fast and flexible image augmentations
Data augmentation is a commonly used technique for increasing both the size
and the diversity of labeled training sets by leveraging input transformations
that preserve output labels. In computer vision domain, image augmentations
have become a common implicit regularization technique to combat overfitting in
deep convolutional neural networks and are ubiquitously used to improve
performance. While most deep learning frameworks implement basic image
transformations, the list is typically limited to some variations and
combinations of flipping, rotating, scaling, and cropping. Moreover, the image
processing speed varies in existing tools for image augmentation. We present
Albumentations, a fast and flexible library for image augmentations with many
various image transform operations available, that is also an easy-to-use
wrapper around other augmentation libraries. We provide examples of image
augmentations for different computer vision tasks and show that Albumentations
is faster than other commonly used image augmentation tools on the most of
commonly used image transformations. The source code for Albumentations is made
publicly available online at https://github.com/albu/albumentation