Skip to main content

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

Project description


docs PyPI - Python Version PyPI license PyPI - Downloads PyPI - Downloads issue resolution open issues

Documents: https://sssegmentation.readthedocs.io/en/latest/

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
UNet Click MICCAI 2015 Click
BEiT Click ICLR 2022 Click
Twins Click NeurIPS 2021 Click
CGNet Click TIP 2020 Click
HRNet Click CVPR 2019 Click
ERFNet Click T-ITS 2017 Click
ResNet Click CVPR 2016 Click
ResNeSt Click ArXiv 2020 Click
ConvNeXt Click CVPR 2022 Click
FastSCNN Click ArXiv 2019 Click
BiSeNetV1 Click ECCV 2018 Click
BiSeNetV2 Click IJCV 2021 Click
MobileNetV2 Click CVPR 2018 Click
MobileNetV3 Click ICCV 2019 Click
SwinTransformer Click ICCV 2021 Click
VisionTransformer Click IClR 2021 Click

Supported Segmentors

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

Supported Datasets

Dataset Project Link Paper Link Code Snippet
LIP Click CVPR 2017 Click
ATR Click ICCV 2015 Click
HRF Click Int J Biomed Sci 2013 Click
CIHP Click ECCV 2018 Click
VSPW Click CVPR 2021 Click
DRIVE Click TMI 2004 Click
STARE Click TMI 2000 Click
ADE20k Click CVPR 2017 Click
MS COCO Click ECCV 2014 Click
MHPv1&v2 Click ArXiv 2017 Click
CHASE DB1 Click TBME 2012 Click
CityScapes Click CVPR 2016 Click
Supervisely Click Website Release 2022 Click
PASCAL VOC Click IJCV 2010 Click
SBUShadow Click ECCV 2016 Click
Dark Zurich Click ICCV 2019 Click
COCOStuff10k Click CVPR 2018 Click
COCOStuff164k Click CVPR 2018 Click
Pascal Context Click CVPR 2014 Click
Nighttime Driving Click ITSC 2018 Click

Citation

If you use this framework in your research, please cite 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}
}

References

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

SSSegmentation-1.3.0.tar.gz (133.3 kB view details)

Uploaded Source

Built Distribution

SSSegmentation-1.3.0-py3-none-any.whl (254.1 kB view details)

Uploaded Python 3

File details

Details for the file SSSegmentation-1.3.0.tar.gz.

File metadata

  • Download URL: SSSegmentation-1.3.0.tar.gz
  • Upload date:
  • Size: 133.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.12.0 pkginfo/1.7.0 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.7.8

File hashes

Hashes for SSSegmentation-1.3.0.tar.gz
Algorithm Hash digest
SHA256 707ccf7f9742a28dbe298e217efd3783143974c57a7f266df3d0acff713faee1
MD5 c9d873f3046c36e35614185e75df90af
BLAKE2b-256 74cc1041feb316591fa1e0a14f36ded642b18fbb508f0d11d23a0f94cb382eda

See more details on using hashes here.

File details

Details for the file SSSegmentation-1.3.0-py3-none-any.whl.

File metadata

  • Download URL: SSSegmentation-1.3.0-py3-none-any.whl
  • Upload date:
  • Size: 254.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.12.0 pkginfo/1.7.0 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.7.8

File hashes

Hashes for SSSegmentation-1.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d8e9f08a33da31ff941c6b57b6852ce46c6b5fdc44aa5b556ec9c8d7adb992a6
MD5 c2e1d14a35a2b1e8e6f5adac065a1909
BLAKE2b-256 4824897fa8aedcfa713c760374a7f6742698dd95ea0918c3dd0b3165a415736f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page