mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
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 -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
16 of 169 committers (9.5%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.9%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Basic Info
- Host: GitHub
- Owner: open-mmlab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://mmsegmentation.readthedocs.io/en/main/
- Size: 43.2 MB
Statistics
- Stars: 9,198
- Watchers: 53
- Forks: 2,752
- Open Issues: 863
- Releases: 45
Topics
Metadata Files
README.md
Introduction
MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
The main branch works with PyTorch 1.6+.
🎉 Introducing MMSegmentation v1.0.0 🎉
We are thrilled to announce the official release of MMSegmentation's latest version! For this new release, the main branch serves as the primary branch, while the development branch is dev-1.x. The stable branch for the previous release remains as the 0.x branch. Please note that the master branch will only be maintained for a limited time before being removed. We encourage you to be mindful of branch selection and updates during use. Thank you for your unwavering support and enthusiasm, and let's work together to make MMSegmentation even more robust and powerful! 💪
MMSegmentation v1.x brings remarkable improvements over the 0.x release, offering a more flexible and feature-packed experience. To utilize the new features in v1.x, we kindly invite you to consult our detailed 📚 migration guide, which will help you seamlessly transition your projects. Your support is invaluable, and we eagerly await your feedback!
Major features
- Unified Benchmark
We provide a unified benchmark toolbox for various semantic segmentation methods.
- Modular Design
We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
- Support of multiple methods out of box
The toolbox directly supports popular and contemporary semantic segmentation frameworks, e.g. PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
- High efficiency
The training speed is faster than or comparable to other codebases.
What's New
v1.2.0 was released on 10/12/2023, from 1.1.0 to 1.2.0, we have added or updated the following features:
Highlights
Support for the open-vocabulary semantic segmentation algorithm SAN
Support monocular depth estimation task, please refer to VPD and Adabins for more details.
- Add new projects: open-vocabulary semantic segmentation algorithm CAT-Seg, real-time semantic segmentation algofithm PP-MobileSeg
Installation
Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.
Get Started
Please see Overview for the general introduction of MMSegmentation.
Please see user guides for the basic usage of MMSegmentation. There are also advanced tutorials for in-depth understanding of mmseg design and implementation .
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
To migrate from MMSegmentation 0.x, please refer to migration.
Tutorial
| Get Started | MMSeg Basic Tutorial | MMSeg Detail Tutorial | MMSeg Development Tutorial |
Benchmark and model zoo
Results and models are available in the model zoo.
Please refer to FAQ for frequently asked questions.
Projects
Here are some implementations of SOTA models and solutions built on MMSegmentation, which are supported and maintained by community users. These projects demonstrate the best practices based on MMSegmentation for research and product development. We welcome and appreciate all the contributions to OpenMMLab ecosystem.
Contributing
We appreciate all contributions to improve MMSegmentation. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
MMSegmentation is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new semantic segmentation methods.
Citation
If you find this project useful in your research, please consider cite:
bibtex
@misc{mmseg2020,
title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
author={MMSegmentation Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
year={2020}
}
License
This project is released under the Apache 2.0 license.
OpenMMLab Family
- MMEngine: OpenMMLab foundational library for training deep learning models.
- MMCV: OpenMMLab foundational library for computer vision.
- MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
- MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMDeploy: OpenMMLab Model Deployment Framework.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MIM: MIM installs OpenMMLab packages.
- Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
Owner
- Name: OpenMMLab
- Login: open-mmlab
- Kind: organization
- Location: China
- Website: https://openmmlab.com
- Twitter: OpenMMLab
- Repositories: 53
- Profile: https://github.com/open-mmlab
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMSegmentation Contributors" title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark" date-released: 2020-07-10 url: "https://github.com/open-mmlab/mmsegmentation" license: Apache-2.0
GitHub Events
Total
- Issues event: 65
- Watch event: 982
- Issue comment event: 125
- Pull request event: 11
- Fork event: 198
Last Year
- Issues event: 65
- Watch event: 982
- Issue comment event: 125
- Pull request event: 11
- Fork event: 198
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| MengzhangLI | m****g@p****n | 153 |
| 谢昕辰 | x****h@o****m | 141 |
| Miao Zheng | 7****g | 101 |
| Jerry Jiarui XU | x****6@g****m | 67 |
| Junjun2016 | h****n@s****n | 52 |
| Rockey | 4****s | 21 |
| 谢昕辰 | x****e@q****m | 21 |
| sennnnn | 5****n | 19 |
| CSH | 4****h | 15 |
| yamengxi | 4****i | 14 |
| Tianlong Ai | 5****g | 13 |
| masaaki | m****5@q****m | 11 |
| linfangjian.vendor | l****n@p****n | 9 |
| sshuair | s****r@g****m | 8 |
| MingJian.L | 4****8 | 8 |
| FangjianLin | 9****1 | 8 |
| Miguel Méndez | m****z@g****m | 7 |
| zhengmiao | z****o@s****m | 7 |
| tianbin li | 4****i | 7 |
| David de la Iglesia Castro | d****o@g****m | 6 |
| Peng Lu | p****7@g****m | 6 |
| Kai Chen | c****v@g****m | 5 |
| Kyungmin Lee | 3****5 | 5 |
| q.yao | y****n@s****m | 4 |
| John Zhu | 3****a | 4 |
| Siddharth Ancha | s****a | 4 |
| Sizheng Guo | 7****9@q****m | 4 |
| jinxianwei | 8****i | 4 |
| Ziyi Wu | d****6@g****m | 3 |
| andife | f****r@a****e | 3 |
| and 139 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 719
- Total pull requests: 219
- Average time to close issues: about 1 month
- Average time to close pull requests: about 1 month
- Total issue authors: 554
- Total pull request authors: 113
- Average comments per issue: 2.25
- Average comments per pull request: 2.54
- Merged pull requests: 107
- Bot issues: 0
- Bot pull requests: 4
Past Year
- Issues: 78
- Pull requests: 15
- Average time to close issues: 18 days
- Average time to close pull requests: 18 days
- Issue authors: 68
- Pull request authors: 10
- Average comments per issue: 0.38
- Average comments per pull request: 1.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jhaggle (7)
- MatCorr (7)
- anirbala98 (6)
- londumas (6)
- Sere1nz (5)
- shinpaul14 (5)
- BalasubramanyamEvani (4)
- zsc1220 (4)
- Saillxl (4)
- sammilei (4)
- catchyoo (4)
- xu19971109 (4)
- zbl929 (4)
- LYKlyk (4)
- yanrihong (4)
Pull Request Authors
- xiexinch (28)
- MengzhangLI (17)
- Junjun2016 (9)
- mmeendez8 (7)
- Ben-Louis (6)
- AI-Tianlong (6)
- tackhwa (5)
- whu-pzhang (5)
- Provable0816 (5)
- Joris-Kuehl-TU-Berlin (4)
- Zoulinx (4)
- RockeyCoss (4)
- dependabot[bot] (4)
- githubuseralpha (4)
- serhiivysotskyi (3)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 70,863 last-month
- Total docker downloads: 1,926
-
Total dependent packages: 13
(may contain duplicates) -
Total dependent repositories: 250
(may contain duplicates) - Total versions: 88
- Total maintainers: 2
pypi.org: mmsegmentation
Open MMLab Semantic Segmentation Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmsegmentation
- Documentation: https://mmsegmentation.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.2.2
published about 2 years ago
Rankings
Maintainers (1)
proxy.golang.org: github.com/open-mmlab/mmsegmentation
- Documentation: https://pkg.go.dev/github.com/open-mmlab/mmsegmentation#section-documentation
- License: apache-2.0
-
Latest release: v1.2.2
published about 2 years ago
Rankings
pypi.org: mmsegmentation-zzb
Open MMLab Semantic Segmentation Toolbox and Benchmark
- Homepage: http://github.com/open-mmlab/mmsegmentation
- Documentation: https://mmsegmentation-zzb.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 0.23.0
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- albumentations >=0.3.2
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx_copybutton *
- sphinx_markdown_tables *
- urllib3 <2.0.0
- mmcv >=2.0.0rc4
- mmengine >=0.5.0,<1.0.0
- cityscapesscripts *
- diffusers *
- einops ==0.3.0
- imageio ==2.9.0
- imageio-ffmpeg ==0.4.2
- invisible-watermark *
- kornia ==0.6
- nibabel *
- omegaconf ==2.1.1
- pudb ==2019.2
- pytorch-lightning ==1.4.2
- streamlit >=0.73.1
- test-tube >=0.7.5
- timm *
- torch-fidelity ==0.3.0
- torchmetrics ==0.6.0
- transformers ==4.19.2
- mmcv >=2.0.0rc1,<2.1.0
- mmengine >=0.4.0,<1.0.0
- prettytable *
- scipy *
- torch *
- torchvision *
- matplotlib *
- numpy *
- packaging *
- prettytable *
- scipy *
- codecov * test
- flake8 * test
- interrogate * test
- pytest * test
- xdoctest >=0.10.0 test
- yapf * test