# AugmenTory: A Fast and Flexible Polygon Augmentation Library

arXiv: https://arxiv.org/abs/2405.04442 · HTML: https://mlsanity.com/paper/2405.04442/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 3 of 3)

- [Image Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2204.08610/) (2022)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels](https://mlsanity.com/paper/2402.05591/) (2024)

## Built on by (most cited first, 1 of 1)

- [Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains](https://mlsanity.com/paper/2603.14764/) (2026)

## Abstract (arXiv)

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