AugmenTory: A Fast and Flexible Polygon Augmentation Library

TL;DR

AugmenTory is a new polygon augmentation library that provides fast, flexible, and resource-efficient data augmentation techniques specifically designed for polygons in image processing tasks like instance segmentation.

Contribution

The paper introduces AugmenTory, a novel polygon augmentation library that reduces computational costs and includes postprocessing features, filling a gap in existing tools.

Findings

Reduces computational time and space compared to existing methods

Includes a postprocessing thresholding feature

Available as an open-source package on GitHub

Abstract

Data augmentation is a key technique for addressing the challenge of limited datasets, which have become a major component in the training procedures of image processing. Techniques such as geometric transformations and color space adjustments have been thoroughly tested for their ability to artificially expand training datasets and generate semi-realistic data for training purposes. Data augmentation is the most important key to addressing the challenge of limited datasets, which have become a major component of image processing training procedures. Data augmentation techniques, such as geometric transformations and color space adjustments, are thoroughly tested for their ability to artificially expand training datasets and generate semi-realistic data for training purposes. Polygons play a crucial role in instance segmentation and have seen a surge in use across advanced models, such as YOLOv8. Despite their growing popularity, the lack of specialized libraries hampers the polygon-augmentation process. This paper introduces a novel solution to this challenge, embodied in the newly developed AugmenTory library. Notably, AugmenTory offers reduced computational demands in both time and space compared to existing methods. Additionally, the library includes a postprocessing thresholding feature. The AugmenTory package is publicly available on GitHub, where interested users can access the source code: https://github.com/Smartory/AugmenTory