SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch
This toolbox provides a practical tool for researchers and developers in computer vision, though it is incremental as it builds upon existing frameworks like MMSegmentation.
The authors introduced SSSegmenation, an open-source PyTorch-based toolbox for supervised semantic image segmentation that is easier to use with fewer dependencies and achieves superior performance compared to MMSegmentation under similar setups.
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}.