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SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch

Project description


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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

Backbone Model Zoo Paper Link Code Snippet
ConvNeXtV2 Click CVPR 2023 Click
MobileViTV2 Click ArXiv 2022 Click
ConvNeXt Click CVPR 2022 Click
MAE Click CVPR 2022 Click
MobileViT Click ICLR 2022 Click
BEiT Click ICLR 2022 Click
Twins Click NeurIPS 2021 Click
SwinTransformer Click ICCV 2021 Click
VisionTransformer Click IClR 2021 Click
BiSeNetV2 Click IJCV 2021 Click
ResNeSt Click ArXiv 2020 Click
CGNet Click TIP 2020 Click
HRNet Click CVPR 2019 Click
MobileNetV3 Click ICCV 2019 Click
FastSCNN Click ArXiv 2019 Click
BiSeNetV1 Click ECCV 2018 Click
MobileNetV2 Click CVPR 2018 Click
ERFNet Click T-ITS 2017 Click
ResNet Click CVPR 2016 Click
UNet Click MICCAI 2015 Click

Supported Segmentors

Segmentor Model Zoo Paper Link Code Snippet
SAMV2 Click ArXiv 2024 Click
EdgeSAM Click ArXiv 2023 Click
IDRNet Click NeurIPS 2023 Click
MobileSAM Click ArXiv 2023 Click
SAMHQ Click NeurIPS 2023 Click
SAM Click ArXiv 2023 Click
MCIBI++ Click TPAMI 2022 Click
Mask2Former Click CVPR 2022 Click
ISNet Click ICCV 2021 Click
MCIBI Click ICCV 2021 Click
MaskFormer Click NeurIPS 2021 Click
Segformer Click NeurIPS 2021 Click
SETR Click CVPR 2021 Click
ISANet Click IJCV 2021 Click
DNLNet Click ECCV 2020 Click
PointRend Click CVPR 2020 Click
OCRNet Click ECCV 2020 Click
GCNet Click TPAMI 2020 Click
APCNet Click CVPR 2019 Click
DMNet Click ICCV 2019 Click
ANNNet Click ICCV 2019 Click
EMANet Click ICCV 2019 Click
FastFCN Click ArXiv 2019 Click
SemanticFPN Click CVPR 2019 Click
CCNet Click ICCV 2019 Click
CE2P Click AAAI 2019 Click
DANet Click CVPR 2019 Click
PSANet Click ECCV 2018 Click
UPerNet Click ECCV 2018 Click
EncNet Click CVPR 2018 Click
Deeplabv3Plus Click ECCV 2018 Click
NonLocalNet Click CVPR 2018 Click
ICNet Click ECCV 2018 Click
Mixed Precision (FP16) Training Click ArXiv 2017 Click
Deeplabv3 Click ArXiv 2017 Click
PSPNet Click CVPR 2017 Click
FCN Click TPAMI 2017 Click

Supported Datasets

Dataset Project Link Paper Link Code Snippet Download Script
VSPW Click CVPR 2021 Click
CMD bash scripts/prepare_datasets.sh vspw
Supervisely Click Website Release 2020 Click
CMD bash scripts/prepare_datasets.sh supervisely
Dark Zurich Click ICCV 2019 Click
CMD bash scripts/prepare_datasets.sh darkzurich
Nighttime Driving Click ITSC 2018 Click
CMD bash scripts/prepare_datasets.sh nighttimedriving
CIHP Click ECCV 2018 Click
CMD bash scripts/prepare_datasets.sh cihp
COCOStuff10k Click CVPR 2018 Click
CMD bash scripts/prepare_datasets.sh cocostuff10k
COCOStuff164k Click CVPR 2018 Click
CMD bash scripts/prepare_datasets.sh coco
MHPv1&v2 Click ArXiv 2017 Click
CMD bash scripts/prepare_datasets.sh mhpv1 & bash scripts/prepare_datasets.sh mhpv2
LIP Click CVPR 2017 Click
CMD bash scripts/prepare_datasets.sh lip
ADE20k Click CVPR 2017 Click
CMD bash scripts/prepare_datasets.sh ade20k
SBUShadow Click ECCV 2016 Click
CMD bash scripts/prepare_datasets.sh sbushadow
CityScapes Click CVPR 2016 Click
CMD bash scripts/prepare_datasets.sh cityscapes
ATR Click ICCV 2015 Click
CMD bash scripts/prepare_datasets.sh atr
Pascal Context Click CVPR 2014 Click
CMD bash scripts/prepare_datasets.sh pascalcontext
MS COCO Click ECCV 2014 Click
CMD bash scripts/prepare_datasets.sh coco
HRF Click Int J Biomed Sci 2013 Click
CMD bash scripts/prepare_datasets.sh hrf
CHASE DB1 Click TBME 2012 Click
CMD bash scripts/prepare_datasets.sh chase_db1
PASCAL VOC Click IJCV 2010 Click
CMD bash scripts/prepare_datasets.sh pascalvoc
DRIVE Click TMI 2004 Click
CMD bash scripts/prepare_datasets.sh drive
STARE Click TMI 2000 Click
CMD bash scripts/prepare_datasets.sh stare

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,

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