# sssegmentation

**Repository Path**: LEE0804/sssegmentation

## Basic Information

- **Project Name**: sssegmentation
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-07
- **Last Updated**: 2021-10-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Introduction
```
sssegmentation is a general framework for our research on strongly supervised semantic segmentation.
```


# Documents
#### In English
https://sssegmentation.readthedocs.io/en/latest/


# Supported
#### Supported Backbones
- [HRNet](https://arxiv.org/pdf/1908.07919.pdf)
- [ResNet](https://arxiv.org/pdf/1512.03385.pdf)
- [ResNeSt](https://arxiv.org/pdf/2004.08955.pdf)
- [MobileNetV2](https://arxiv.org/pdf/1801.04381.pdf)
- [MobileNetV3](https://arxiv.org/pdf/1905.02244.pdf)
- [SwinTransformer](https://arxiv.org/pdf/2103.14030.pdf)
- [VisionTransformer](https://arxiv.org/pdf/2010.11929.pdf)
#### Supported Models
- [FCN](https://arxiv.org/pdf/1411.4038.pdf)
- [CE2P](https://arxiv.org/pdf/1809.05996.pdf)
- [SETR](https://arxiv.org/pdf/2012.15840.pdf)
- [ISNet](https://arxiv.org/pdf/2108.12382.pdf)
- [CCNet](https://arxiv.org/pdf/1811.11721.pdf)
- [DANet](https://arxiv.org/pdf/1809.02983.pdf)
- [GCNet](https://arxiv.org/pdf/1904.11492.pdf)
- [DMNet](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)
- [EncNet](https://arxiv.org/pdf/1803.08904.pdf)
- [OCRNet](https://arxiv.org/pdf/1909.11065.pdf)
- [DNLNet](https://arxiv.org/pdf/2006.06668.pdf)
- [ANNNet](https://arxiv.org/pdf/1908.07678.pdf)
- [EMANet](https://arxiv.org/pdf/1907.13426.pdf)
- [PSPNet](https://arxiv.org/pdf/1612.01105.pdf)
- [PSANet](https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf)
- [APCNet](https://openaccess.thecvf.com/content_CVPR_2019/papers/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.pdf)
- [UPerNet](https://arxiv.org/pdf/1807.10221.pdf)
- [Deeplabv3](https://arxiv.org/pdf/1706.05587.pdf)
- [Segformer](https://arxiv.org/pdf/2105.15203.pdf)
- [SemanticFPN](https://arxiv.org/pdf/1901.02446.pdf)
- [NonLocalNet](https://arxiv.org/pdf/1711.07971.pdf)
- [Deeplabv3Plus](https://arxiv.org/pdf/1802.02611.pdf)
- [MemoryNet-MCIBI](https://arxiv.org/pdf/2108.11819.pdf)
#### Supported Datasets
- [LIP](http://sysu-hcp.net/lip/)
- [ATR](http://sysu-hcp.net/lip/overview.php)
- [CIHP](http://sysu-hcp.net/lip/overview.php)
- [ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/)
- [MS COCO](https://cocodataset.org/#home)
- [MHPv1&v2](https://lv-mhp.github.io/dataset)
- [CityScapes](https://www.cityscapes-dataset.com/)
- [Supervisely](https://supervise.ly/explore/projects/supervisely-person-dataset-23304/datasets)
- [SBUShadow](https://www3.cs.stonybrook.edu/~cvl/projects/shadow_noisy_label/index.html)
- [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)
- [COCOStuff10k](https://cocodataset.org/#home)
- [Pascal Context](https://cs.stanford.edu/~roozbeh/pascal-context/)


# Citation
If you use this framework in your research, please cite this project:
```
@misc{ssseg2020,
    author = {Zhenchao Jin},
    title = {SSSegmentation: A general framework for strongly supervised semantic segmentation},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/SegmentationBLWX/sssegmentation}},
}

@article{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},
  journal={arXiv preprint arXiv:2108.12382},
  year={2021}
}

@article{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},
  journal={arXiv preprint arXiv:2108.11819},
  year={2021}
}
```


# References
```
[1]. https://github.com/open-mmlab/mmcv
[2]. https://github.com/open-mmlab/mmsegmentation
```