Science Score: 46.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
22 of 453 committers (4.9%) 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
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Repository
OpenMMLab Detection Toolbox and Benchmark
Basic Info
- Host: GitHub
- Owner: open-mmlab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://mmdetection.readthedocs.io
- Size: 61.8 MB
Statistics
- Stars: 31,603
- Watchers: 370
- Forks: 9,722
- Open Issues: 1,933
- Releases: 53
Topics
Metadata Files
README.md
Introduction
MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.
The main branch works with PyTorch 1.8+.
Major features
- **Modular Design** We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. - **Support of multiple tasks out of box** The toolbox directly supports multiple detection tasks such as **object detection**, **instance segmentation**, **panoptic segmentation**, and **semi-supervised object detection**. - **High efficiency** All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). - **State of the art** The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. The newly released [RTMDet](configs/rtmdet) also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.Apart from MMDetection, we also released MMEngine for model training and MMCV for computer vision research, which are heavily depended on by this toolbox.
What's New
We have released the pre-trained weights for MM-Grounding-DINO Swin-B and Swin-L, welcome to try and give feedback.
Highlight
v3.3.0 was released in 5/1/2024:
MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Grounding DINO is a grounding pre-training model that unifies 2d open vocabulary object detection and phrase grounding, with wide applications. However, its training part has not been open sourced. Therefore, we propose MM-Grounding-DINO, which not only serves as an open source replication version of Grounding DINO, but also achieves significant performance improvement based on reconstructed data types, exploring different dataset combinations and initialization strategies. Moreover, we conduct evaluations from multiple dimensions, including OOD, REC, Phrase Grounding, OVD, and Fine-tune, to fully excavate the advantages and disadvantages of Grounding pre-training, hoping to provide inspiration for future work.
code: mmgroundingdino/README.md
We are excited to announce our latest work on real-time object recognition tasks, RTMDet, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the technical report. Pre-trained models are here.
| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) | | ------------------------ | ------- | ------------------------------------ | ---------------------- | | Object Detection | COCO | 52.8 | 322 | | Instance Segmentation | COCO | 44.6 | 188 | | Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |
Installation
Please refer to Installation for installation instructions.
Getting Started
Please see Overview for the general introduction of MMDetection.
For detailed user guides and advanced guides, please refer to our documentation:
- User Guides
- Train & Test
- Learn about Configs
- Inference with existing models
- Dataset Prepare
- Test existing models on standard datasets
- Train predefined models on standard datasets
- Train with customized datasets
- Train with customized models and standard datasets
- Finetuning Models
- Test Results Submission
- Weight initialization
- Use a single stage detector as RPN
- Semi-supervised Object Detection
- Useful Tools
- Advanced Guides
We also provide object detection colab tutorial 
and instance segmentation colab tutorial .
To migrate from MMDetection 2.x, please refer to migration.
Overview of Benchmark and Model Zoo
Results and models are available in the model zoo.
| Backbones | Necks | Loss | Common |
|
Some other methods are also supported in projects using MMDetection.
FAQ
Please refer to FAQ for frequently asked questions.
Contributing
We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.
Citation
If you use this toolbox or benchmark in your research, please cite this project.
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal= {arXiv preprint arXiv:1906.07155},
year={2019}
}
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.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMYOLO: OpenMMLab YOLO series 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.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.
- MIM: MIM installs OpenMMLab packages.
- MMEval: A unified evaluation library for multiple machine learning libraries.
- 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
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Kai Chen | c****v@g****m | 311 |
| Haian Huang(深度眸) | 1****9@q****m | 235 |
| Wenwei Zhang | 4****e | 189 |
| Cao Yuhang | y****6@g****m | 156 |
| RangiLyu | l****i@g****m | 127 |
| Jerry Jiarui XU | x****6@g****m | 109 |
| BigDong | y****g@t****n | 83 |
| Cedric Luo | l****6@o****m | 67 |
| jbwang1997 | j****7@g****m | 60 |
| Shilong Zhang | 6****g | 58 |
| Czm369 | 4****9 | 52 |
| pangjm | p****u@g****m | 49 |
| Guangchen Lin | 3****0@q****m | 40 |
| ThangVu | t****k@g****m | 32 |
| Wang Xinjiang | w****g@s****m | 30 |
| Jiaqi Wang | 1****0@l****k | 30 |
| Yue Zhou | 5****9@q****m | 29 |
| zwhus | 1****s | 26 |
| Qiaofei Li | q****i@g****m | 22 |
| Jon Crall | e****c@g****m | 21 |
| Yosuke Shinya | 4****y | 19 |
| RunningLeon | m****g@s****m | 18 |
| wanghonglie | w****e@p****n | 17 |
| Range King | R****Z@g****m | 15 |
| tianyuandu | t****u@g****m | 13 |
| jason_w | w****g@1****m | 11 |
| David de la Iglesia Castro | d****o@g****m | 11 |
| Ryan Li | x****e@c****k | 10 |
| Sanbu | 9****y | 9 |
| Maxim Bonnaerens | m****m@b****e | 9 |
| and 423 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 1,695
- Total pull requests: 380
- Average time to close issues: about 1 month
- Average time to close pull requests: 3 months
- Total issue authors: 1,321
- Total pull request authors: 182
- Average comments per issue: 2.5
- Average comments per pull request: 2.24
- Merged pull requests: 132
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 224
- Pull requests: 41
- Average time to close issues: 6 days
- Average time to close pull requests: 2 days
- Issue authors: 186
- Pull request authors: 24
- Average comments per issue: 0.39
- Average comments per pull request: 0.68
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- softwaresatakan133 (34)
- GeorgePearse (18)
- ZCTraveon (10)
- ufamugnihjkhg (10)
- MiosotisYamauchi (8)
- dcjaselin (8)
- wu33learn (8)
- zadeziray (7)
- AndreaPi (7)
- dzdayle (7)
- moamandra (7)
- dmspellacy (7)
- matthost (6)
- shani-sony (6)
- lijoe123 (6)
Pull Request Authors
- hhaAndroid (44)
- zwhus (19)
- RangeKing (8)
- Jun0922 (6)
- Baboom-l (5)
- chhluo (5)
- AwePhD (5)
- Cycyes (5)
- MambaWong (5)
- Li-Qingyun (5)
- chatzianagnostis (5)
- okotaku (4)
- PhoenixZ810 (4)
- AlanBlanchet (4)
- Shengshenlan (4)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 8
-
Total downloads:
- pypi 258,977 last-month
- Total docker downloads: 14,870
-
Total dependent packages: 39
(may contain duplicates) -
Total dependent repositories: 649
(may contain duplicates) - Total versions: 117
- Total maintainers: 5
pypi.org: mmdet
OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://mmdet.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 3.3.0
published about 2 years ago
Rankings
proxy.golang.org: github.com/open-mmlab/mmdetection
- Documentation: https://pkg.go.dev/github.com/open-mmlab/mmdetection#section-documentation
- License: apache-2.0
-
Latest release: v3.3.0+incompatible
published about 2 years ago
Rankings
pypi.org: mmps
OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://mmps.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 2.4.0
published about 3 years ago
Rankings
Maintainers (1)
pypi.org: psvision
OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://psvision.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 2.4.0
published almost 3 years ago
Rankings
pypi.org: psvis
OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://psvis.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.6.0
published almost 3 years ago
Rankings
Maintainers (1)
conda-forge.org: mmdet
MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.
- Homepage: https://github.com/open-mmlab/mmdetection
- License: Apache-2.0
-
Latest release: 2.25.3
published over 3 years ago
Rankings
pypi.org: avi-mmdet
Custom OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://avi-mmdet.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 3.3.2
published about 1 year ago
Rankings
Maintainers (1)
pypi.org: mmdet-open
OpenMMLab Detection Toolbox and Benchmark
- Homepage: https://github.com/open-mmlab/mmdetection
- Documentation: https://mmdet-open.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 3.3.0
published over 1 year ago
Rankings
Maintainers (1)
Dependencies
- albumentations >=0.3.2
- cython *
- numpy *
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- sphinx_rtd_theme ==0.5.2
- mmcv-full >=1.3.17
- cityscapesscripts *
- imagecorruptions *
- scipy *
- sklearn *
- timm *
- mmcv *
- torch *
- torchvision *
- matplotlib *
- numpy *
- pycocotools *
- six *
- terminaltables *
- asynctest * test
- codecov * test
- flake8 * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- protobuf <=3.20.1 test
- pytest * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test