This paper presents SSSegmenation, which is an open source supervised semantic image segmentation toolbox based on PyTorch. The design of this toolbox is motivated by MMSegmentation while it is easier to use because of fewer dependencies and achieves superior segmentation performance under a comparable training and testing setup. Moreover, the toolbox also provides plenty of trained weights for popular and contemporary semantic segmentation methods, including Deeplab, PSPNet, OCRNet, MaskFormer, \emph{etc}. We expect that this toolbox can contribute to the future development of semantic segmentation. Codes and model zoos are available at \href{https://github.com/SegmentationBLWX/sssegmentation/}{SSSegmenation}.

翻译：本文介绍了SSSegmenation，这是一个基于PyTorch的开源监督语义图像分割工具包。该工具包的设计灵感源于MMSegmentation，但由于依赖项更少而更易于使用，并且在可比较的训练和测试设置下实现了更优的分割性能。此外，该工具包还对包括Deeplab、PSPNet、OCRNet、MaskFormer等在内的流行及当代语义分割方法提供了大量预训练权重。我们期望该工具包能为语义分割的未来发展做出贡献。代码与模型库可在\href{https://github.com/SegmentationBLWX/sssegmentation/}{SSSegmenation}获取。