Documents: https://sssegmentation.readthedocs.io/en/latest/
What’s New
Introduction
SSSegmentation is an open source supervised semantic segmentation toolbox based on PyTorch.
You can star this repository to keep track of the project if it’s helpful for you, thank you for your support.
Major Features
-
High Performance
The performance of re-implemented segmentation algorithms is better than or comparable to other codebases.
-
Modular Design and Unified Benchmark
Various segmentation methods are unified into several specific modules.
Benefiting from this design, SSSegmentation can integrate a great deal of popular and contemporary semantic segmentation frameworks and then, train and test them on unified benchmarks.
-
Fewer Dependencies
SSSegmenation tries its best to avoid introducing more dependencies when reproducing novel semantic segmentation approaches.
Benchmark and Model Zoo
Supported Backbones
Supported Segmentors
Supported Datasets
Citation
If you use SSSegmentation in your research, please consider citing this project,
@article{jin2023sssegmenation,
title={SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch},
author={Jin, Zhenchao},
journal={arXiv preprint arXiv:2305.17091},
year={2023}
}
@inproceedings{jin2021isnet,
title={ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation},
author={Jin, Zhenchao and Liu, Bin and Chu, Qi and Yu, Nenghai},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={7189--7198},
year={2021}
}
@inproceedings{jin2021mining,
title={Mining Contextual Information Beyond Image for Semantic Segmentation},
author={Jin, Zhenchao and Gong, Tao and Yu, Dongdong and Chu, Qi and Wang, Jian and Wang, Changhu and Shao, Jie},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={7231--7241},
year={2021}
}
@article{jin2022mcibi++,
title={MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation},
author={Jin, Zhenchao and Yu, Dongdong and Yuan, Zehuan and Yu, Lequan},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
@inproceedings{jin2023idrnet,
title={IDRNet: Intervention-Driven Relation Network for Semantic Segmentation},
author={Jin, Zhenchao and Hu, Xiaowei and Zhu, Lingting and Song, Luchuan and Yuan, Li and Yu, Lequan},
booktitle={Thirty-Seventh Conference on Neural Information Processing Systems},
year={2023}
}
References
We are very grateful to the following projects for their help in building SSSegmentation,
评论