ssseg_thd

https://github.com/huadongtang/ssseg_thd

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
Links to: arxiv.org, ieee.org
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (11.4%) to scientific vocabulary

Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: HuadongTang
License: apache-2.0
Language: Python
Default Branch: main
Size: 3.26 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Created about 2 years ago · Last pushed about 2 years ago

Metadata Files

Readme Funding License Citation

README.md

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

Supported Segmentors

Supported Datasets

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

Owner

Name: THD
Login: HuadongTang
Kind: user
Location: Sydney
Company: UTS

Repositories: 2
Profile: https://github.com/HuadongTang

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "Zhenchao Jin"
title: "SSSegmentation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch"
date-released: 2020-10-23
url: "https://github.com/SegmentationBLWX/sssegmentation"
license: Apache-2.0

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