# SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch

Source: [SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch](<https://www.sota2.com/research/paper/68fa6f811e209131cea460f6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:21:27.030Z

Authors: Zhenchao Jin

Published: 2023-05-26T17:02:42.000Z

## Abstract

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}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.17091>)

[PDF](<https://arxiv.org/pdf/2305.17091.pdf>)