  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
