AugmenTory：一个快速且灵活的多边形增强库
AugmenTory: A Fast and Flexible Polygon Augmentation Library
AI总结：
针对实例分割中多边形标注缺乏专用增强工具的问题，本文提出开源 AugmenTory 库，支持快速灵活的多边形增强与后处理阈值化，并降低时间和空间计算开销。
摘要：
数据增强是应对有限数据集挑战的关键技术，而有限数据集已成为图像处理训练流程中的主要组成部分。几何变换和色彩空间调整等技术，因其能够人工扩展训练数据集并生成用于训练的半真实数据而得到充分测试。数据增强是解决有限数据集挑战的最重要关键，有限数据集已成为图像处理训练流程的重要组成部分。数据增强技术（如几何变换和色彩空间调整）经过充分测试，能够人工扩展训练数据集并生成用于训练目的的半真实数据。多边形在实例分割中发挥着关键作用，并在 YOLOv8 等先进模型中的使用迅速增加。尽管多边形日益普及，但专用库的缺乏阻碍了多边形增强流程。本文介绍了针对这一挑战的新型解决方案，即新开发的 AugmenTory 库。值得注意的是，与现有方法相比，AugmenTory 在时间和空间两方面都降低了计算需求。此外，该库包含后处理阈值化功能。AugmenTory 包已在 GitHub 上公开发布，感兴趣的用户可访问源代码：https://github.com/Smartory/AugmenTory
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
