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
2 of 114 committers (1.8%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.5%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
OpenMMLab Model Deployment Framework
Basic Info
- Host: GitHub
- Owner: open-mmlab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://mmdeploy.readthedocs.io/en/latest/
- Size: 13.2 MB
Statistics
- Stars: 3,021
- Watchers: 35
- Forks: 676
- Open Issues: 444
- Releases: 24
Topics
Metadata Files
README.md
Highlights
The MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Please align the version when using it.
The default branch has been switched to main from master. MMDeploy 0.x (master) will be deprecated and new features will only be added to MMDeploy 1.x (main) in future.
| mmdeploy | mmengine | mmcv | mmdet | others | | :------: | :------: | :------: | :------: | :----: | | 0.x.y | - | <=1.x.y | <=2.x.y | 0.x.y | | 1.x.y | 0.x.y | 2.x.y | 3.x.y | 1.x.y |
deploee offers over 2,300 AI models in ONNX, NCNN, TRT and OpenVINO formats. Featuring a built-in list of real hardware devices, deploee enables users to convert Torch models into any target inference format for profiling purposes.
Introduction
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
Main features
Fully support OpenMMLab models
The currently supported codebases and models are as follows, and more will be included in the future
Multiple inference backends are available
The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.
The benchmark can be found from here
| Device / Platform |
Linux | Windows | macOS | Android |
|---|---|---|---|---|
| x86_64 CPU |
 onnxruntime  pplnn  ncnn  LibTorch  OpenVINO TVM |
onnxruntime OpenVINO ncnn |
- | - |
| ARM CPU |
|
- | - |
ncnn |
| RISC-V |
 ncnn |
- | - | - |
| NVIDIA GPU |
LibTorch pplnn |
|
- | - |
| NVIDIA Jetson |
TensorRT |
- | - | - |
| Huawei ascend310 |
 CANN |
- | - | - |
| Rockchip |
 RKNN |
- | - | - |
| Apple M1 | - | - |
 CoreML |
- |
| Adreno GPU |
- | - | - |
 SNPE ncnn |
| Hexagon DSP |
- | - | - |
SNPE |
Efficient and scalable C/C++ SDK Framework
All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on
Documentation
Please read getting_started for the basic usage of MMDeploy. We also provide tutoials about:
- Build
- User Guide
- Developer Guide
- Custom Backend Ops
- FAQ
- Contributing
Benchmark and Model zoo
You can find the supported models from here and their performance in the benchmark.
Contributing
We appreciate all contributions to MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
We would like to sincerely thank the following teams for their contributions to MMDeploy:
Citation
If you find this project useful in your research, please consider citing:
BibTeX
@misc{=mmdeploy,
title={OpenMMLab's Model Deployment Toolbox.},
author={MMDeploy Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
year={2021}
}
License
This project is released under the Apache 2.0 license.
Projects in OpenMMLab
- 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.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark
- 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." title: "OpenMMLab's Model deployment toolbox" authors: - name: "MMDeploy Contributors" date-released: 2021-12-27 url: "https://github.com/open-mmlab/mmdeploy" license: Apache-2.0
GitHub Events
Total
- Issues event: 73
- Watch event: 255
- Issue comment event: 190
- Pull request event: 3
- Fork event: 64
Last Year
- Issues event: 73
- Watch event: 255
- Issue comment event: 190
- Pull request event: 3
- Fork event: 64
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| RunningLeon | m****g@y****t | 159 |
| AllentDan | 4****n | 127 |
| q.yao | s****o@l****m | 103 |
| hanrui1sensetime | 8****e | 86 |
| q.yao | y****n@s****m | 74 |
| Li Zhang | l****9@g****m | 68 |
| Chen Xin | x****u@g****m | 68 |
| lvhan028 | l****8@1****m | 67 |
| tpoisonooo | k****n@a****m | 48 |
| Yifan Zhou | s****e@1****m | 43 |
| VVsssssk | 8****k | 36 |
| triple-Mu | g****u@1****m | 22 |
| Semyon Bevzyuk | s****k@g****m | 15 |
| huayuan4396 | 1****6 | 9 |
| HinGwenWoong | p****3@q****m | 8 |
| Mengyang Liu | 4****g | 6 |
| Johannes L | t****e | 5 |
| Qingren | 4****n | 4 |
| Xin Li | 7****7 | 4 |
| Yue Zhou | 5****9@q****m | 4 |
| vansin | m****e@1****m | 3 |
| Zaida Zhou | 5****a | 3 |
| Ryan_Huang | 4****g | 3 |
| Mohammed Yasin | 3****G | 3 |
| Yang Nie | f****c@o****m | 2 |
| 任祉涵 | 5****n | 2 |
| zambranohally | 6****y | 2 |
| Zhiqiang Wang | z****g@f****m | 2 |
| SsTtOoNnEe | 1****0@q****m | 2 |
| Shengxi Li | 9****6@q****m | 2 |
| and 84 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 699
- Total pull requests: 152
- Average time to close issues: about 1 month
- Average time to close pull requests: 12 days
- Total issue authors: 491
- Total pull request authors: 57
- Average comments per issue: 4.14
- Average comments per pull request: 2.74
- Merged pull requests: 103
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 68
- Pull requests: 4
- Average time to close issues: 1 day
- Average time to close pull requests: N/A
- Issue authors: 58
- Pull request authors: 3
- Average comments per issue: 0.34
- Average comments per pull request: 0.75
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- GeorgePearse (8)
- fpeanut (7)
- vicnoah (7)
- gitleej (7)
- lijoe123 (6)
- kdavidlp123 (6)
- da13132 (6)
- Daanfb (6)
- a819411321 (5)
- kelvinwang139 (5)
- leemayi (5)
- gstariarch (5)
- GiovanniFyc (4)
- happybear1015 (4)
- azuryl (4)
Pull Request Authors
- RunningLeon (24)
- irexyc (15)
- grimoire (13)
- AllentDan (13)
- huayuan4396 (9)
- Boomerl (5)
- tpoisonooo (4)
- Baboom-l (4)
- Y-T-G (3)
- take-cheeze (2)
- rofgmd (2)
- hiramf (2)
- mys007 (2)
- ry3s (2)
- yinfan98 (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 6
-
Total downloads:
- pypi 22,477 last-month
- Total docker downloads: 42
-
Total dependent packages: 3
(may contain duplicates) -
Total dependent repositories: 2
(may contain duplicates) - Total versions: 43
- Total maintainers: 3
pypi.org: mmdeploy
OpenMMLab Model Deployment
- Homepage: https://github.com/open-mmlab/mmdeploy
- Documentation: https://mmdeploy.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.3.1
published about 2 years ago
Rankings
Maintainers (1)
proxy.golang.org: github.com/open-mmlab/mmdeploy
- Documentation: https://pkg.go.dev/github.com/open-mmlab/mmdeploy#section-documentation
- License: apache-2.0
-
Latest release: v1.3.1
published about 2 years ago
Rankings
pypi.org: mmdeploy-runtime
OpenMMLab Model Deployment SDK python api
- Homepage: https://github.com/open-mmlab/mmdeploy
- Documentation: https://mmdeploy-runtime.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.3.1
published about 2 years ago
Rankings
Maintainers (1)
pypi.org: otxdeploy
(Temp fork for PyPI packaging) OpenMMLab Model Deployment
- Homepage: https://github.com/open-mmlab/mmdeploy
- Documentation: https://otxdeploy.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 0.14.1
published about 2 years ago
Rankings
Maintainers (1)
pypi.org: mmdeploy-runtime-gpu
OpenMMLab Model Deployment SDK python api
- Homepage: https://github.com/open-mmlab/mmdeploy
- Documentation: https://mmdeploy-runtime-gpu.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.3.1
published about 2 years ago
Rankings
Maintainers (1)
pypi.org: mmdeploy-vitrox
OpenMMLab Model Deployment
- Homepage: https://github.com/open-mmlab/mmdeploy
- Documentation: https://mmdeploy-vitrox.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.3.1
published 12 months ago