  Semantic segmentation plays a vital role in computer vision tasks, enabling
precise pixel-level understanding of images. In this paper, we present a
comprehensive library for semantic segmentation, which contains implementations
of popular segmentation models like SegNet, FCN, UNet, and PSPNet. We also
evaluate and compare these models on several datasets, offering researchers and
practitioners a powerful toolset for tackling diverse segmentation challenges.
