Documents: https://sssegmentation.readthedocs.io/en/latest/
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.
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Unified Benchmark
We provide a unified benchmark toolbox for various semantic segmentation methods.
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Modular Design
We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
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Support of Multiple Methods Out of Box
The toolbox directly supports popular and contemporary semantic segmentation frameworks, e.g., ISNet, DeepLabV3, PSPNet, MCIBI, etc.
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High Performance
The segmentation performance is better than or comparable to other codebases.
- UNet
- BEiT
- Twins
- CGNet
- HRNet
- ERFNet
- ResNet
- ResNeSt
- ConvNeXt
- FastSCNN
- BiSeNetV1
- BiSeNetV2
- MobileNetV2
- MobileNetV3
- SwinTransformer
- VisionTransformer
- FCN
- CE2P
- SETR
- ISNet
- ICNet
- CCNet
- DANet
- GCNet
- DMNet
- ISANet
- EncNet
- OCRNet
- DNLNet
- ANNNet
- EMANet
- PSPNet
- PSANet
- APCNet
- FastFCN
- UPerNet
- PointRend
- Deeplabv3
- Segformer
- MaskFormer
- SemanticFPN
- NonLocalNet
- Deeplabv3Plus
- MemoryNet-MCIBI
- MemoryNet-MCIBI++
- Mixed Precision (FP16) Training
- LIP
- ATR
- HRF
- CIHP
- VSPW
- DRIVE
- STARE
- ADE20k
- MS COCO
- MHPv1&v2
- CHASE DB1
- CityScapes
- Supervisely
- SBUShadow
- PASCAL VOC
- COCOStuff10k
- COCOStuff164k
- Pascal Context
If you use this framework in your research, please cite this project:
@misc{ssseg2020,
author = {Zhenchao Jin},
title = {SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SegmentationBLWX/sssegmentation}},
}
@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}
}
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