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

翻译：数据增强是解决数据集有限问题的关键技术，已成为图像处理训练流程中的重要组成部分。几何变换和色彩空间调整等技术已被充分验证，能够人为扩展训练数据集并生成半逼真数据用于训练。数据增强是解决数据集有限问题的最关键手段，已成为图像处理训练流程的核心要素。几何变换和色彩空间调整等数据增强技术因其在人为扩展训练数据集及生成半逼真训练数据方面的能力而得到充分验证。多边形在实例分割中扮演关键角色，并在YOLOv8等先进模型中的应用日益增多。尽管多边形应用日益广泛，但专用库的缺乏阻碍了多边形增强流程的发展。本文针对该问题提出了一种创新解决方案，即新研发的AugmenTory库。值得注意的是，与现有方法相比，AugmenTory在时间和空间上的计算需求更低。此外，该库还包含后处理阈值设定功能。AugmenTory软件包已在GitHub上公开，用户可访问以下链接获取源代码：https://github.com/Smartory/AugmenTory