SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch
Project description
Documents: https://sssegmentation.readthedocs.io/en/latest/
What's New
- 2024-08-05: Support SAMV2, refer to inference-with-samv2 for more details.
- 2023-12-20: Support EdgeSAM and SAMHQ, refer to inference-with-edgesam and inference-with-samhq for more details.
- 2023-10-25: Support ConvNeXtV2, refer to Results and Models for ConvNeXtV2 for more details.
- 2023-10-23: Support MobileViT and MobileViTV2, refer to Results and Models for MobileViT for more details.
- 2023-10-18: Support Mask2Former, refer to Results and Models for Mask2Former for more details.
- 2023-10-17: We release the source codes of IDRNet: Intervention-Driven Relation Network for Semantic Segmentation, which was accepted by NeurIPS 2023, refer to Results and Models for IDRNet for more details.
- 2023-10-15: Support MobileSAM, refer to inference-with-mobilesam for more details.
- 2023-09-27: Support SAM, refer to inference-with-sam for more details.
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
Supported Datasets
Dataset | Project Link | Paper Link | Code Snippet | Download Script |
---|---|---|---|---|
VSPW | Click | CVPR 2021 | Click | CMDbash scripts/prepare_datasets.sh vspw |
Supervisely | Click | Website Release 2020 | Click | CMDbash scripts/prepare_datasets.sh supervisely |
Dark Zurich | Click | ICCV 2019 | Click | CMDbash scripts/prepare_datasets.sh darkzurich |
Nighttime Driving | Click | ITSC 2018 | Click | CMDbash scripts/prepare_datasets.sh nighttimedriving |
CIHP | Click | ECCV 2018 | Click | CMDbash scripts/prepare_datasets.sh cihp |
COCOStuff10k | Click | CVPR 2018 | Click | CMDbash scripts/prepare_datasets.sh cocostuff10k |
COCOStuff164k | Click | CVPR 2018 | Click | CMDbash scripts/prepare_datasets.sh coco |
MHPv1&v2 | Click | ArXiv 2017 | Click | CMDbash scripts/prepare_datasets.sh mhpv1 & bash scripts/prepare_datasets.sh mhpv2 |
LIP | Click | CVPR 2017 | Click | CMDbash scripts/prepare_datasets.sh lip |
ADE20k | Click | CVPR 2017 | Click | CMDbash scripts/prepare_datasets.sh ade20k |
SBUShadow | Click | ECCV 2016 | Click | CMDbash scripts/prepare_datasets.sh sbushadow |
CityScapes | Click | CVPR 2016 | Click | CMDbash scripts/prepare_datasets.sh cityscapes |
ATR | Click | ICCV 2015 | Click | CMDbash scripts/prepare_datasets.sh atr |
Pascal Context | Click | CVPR 2014 | Click | CMDbash scripts/prepare_datasets.sh pascalcontext |
MS COCO | Click | ECCV 2014 | Click | CMDbash scripts/prepare_datasets.sh coco |
HRF | Click | Int J Biomed Sci 2013 | Click | CMDbash scripts/prepare_datasets.sh hrf |
CHASE DB1 | Click | TBME 2012 | Click | CMDbash scripts/prepare_datasets.sh chase_db1 |
PASCAL VOC | Click | IJCV 2010 | Click | CMDbash scripts/prepare_datasets.sh pascalvoc |
DRIVE | Click | TMI 2004 | Click | CMDbash scripts/prepare_datasets.sh drive |
STARE | Click | TMI 2000 | Click | CMDbash 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,
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file SSSegmentation-1.5.4.tar.gz
.
File metadata
- Download URL: SSSegmentation-1.5.4.tar.gz
- Upload date:
- Size: 233.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.7.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1dcbc5fd9661ffebc918cf28b8289fa8b758e143c51369e00546de2e5f0d5c01 |
|
MD5 | fcad25214ae907f5bdf84c9396162d34 |
|
BLAKE2b-256 | 97f043453eee6d858932b1949a36e29f61358de870b048ddd9d3d3121d0356f7 |