SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch
Zhenchao Jin
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}.
Related Papers
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction2025-07-21Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction2025-07-17DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model2025-07-17From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation2025-07-17Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion2025-07-17SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation2025-07-17Unified Medical Image Segmentation with State Space Modeling Snake2025-07-17A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique2025-07-17