Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (13.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Zhengfei-0311
- License: apache-2.0
- Language: Python
- Default Branch: master
- Size: 15.2 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
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 master branch works with PyTorch 1.6+.
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.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
v3.0.0rc2 was released in 21/10/2022:
- Support imagenet pre-training for RTMDet's backbone.
- Refactor standard roi head with
box type.
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.
- MIM: MIM installs OpenMMLab packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- 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.
Owner
- Login: Zhengfei-0311
- Kind: user
- Repositories: 25
- Profile: https://github.com/Zhengfei-0311
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMDetection Contributors" title: "OpenMMLab Detection Toolbox and Benchmark" date-released: 2018-08-22 url: "https://github.com/open-mmlab/mmdetection" license: Apache-2.0
GitHub Events
Total
Last Year
Dependencies
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- asynctest *
- cityscapesscripts *
- codecov *
- cython *
- flake8 *
- imagecorruptions *
- instaboostfast *
- interrogate *
- isort ==4.3.21
- kwarray *
- matplotlib *
- memory_profiler *
- mmcv <2.1.0,>=2.0.0rc1
- mmengine <1.0.0,>=0.1.0
- mmtrack *
- numpy *
- onnx ==1.7.0
- onnxruntime >=1.8.0
- parameterized *
- protobuf <=3.20.1
- psutil *
- pycocotools *
- pytest *
- scipy *
- six *
- sklearn *
- terminaltables *
- ubelt *
- xdoctest >=0.10.0
- yapf *
- 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 >=2.0.0rc1,<2.1.0
- mmengine >=0.1.0,<1.0.0
- cityscapesscripts *
- imagecorruptions *
- scipy *
- sklearn *
- mmcv *
- torch *
- torchvision *
- matplotlib *
- numpy *
- pycocotools *
- six *
- terminaltables *
- asynctest * test
- cityscapesscripts * test
- codecov * test
- flake8 * test
- imagecorruptions * test
- instaboostfast * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- memory_profiler * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- parameterized * test
- protobuf <=3.20.1 test
- psutil * test
- pytest * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test