Documents: https://sssegmentation.readthedocs.io/en/latest/
Introduction
SSSegmentation is an open source strongly 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.
Supported
Supported Backbones
- UNet
- Twins
- CGNet
- HRNet
- ERFNet
- ResNet
- ResNeSt
- FastSCNN
- BiSeNetV1
- BiSeNetV2
- MobileNetV2
- MobileNetV3
- SwinTransformer
- VisionTransformer
Supported Models
- 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
- Mixed Precision (FP16) Training
Supported Datasets
- LIP
- ATR
- HRF
- CIHP
- VSPW
- DRIVE
- STARE
- ADE20k
- MS COCO
- MHPv1&v2
- CHASE DB1
- CityScapes
- Supervisely
- SBUShadow
- PASCAL VOC
- COCOStuff10k
- COCOStuff164k
- Pascal Context
Citation
If you use this framework in your research, please cite this project:
@misc{ssseg2020,
author = {Zhenchao Jin},
title = {SSSegmentation: An Open Source Strongly 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}
}