怎么看mmdetection版本_mmdetection: mmdetection 是一个基于 PyTorch 的开源对象检测工具箱，它提供了已公开发表的多种视觉检测核心模块，通过这些模块的组合，可以...
News: We released the technical report on ArXiv.
Introduction
MMDetection is an open source object detection toolbox based on PyTorch. It is
a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK.
The master branch works with PyTorch 1.3 to 1.6.
The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.
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 frameworks out of box
The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.
High efficiency
All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet.
State of the art
The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.
Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.
License
This project is released under the Apache 2.0 license.
Changelog
v2.6.0 was released in 1/11/2020.
Please refer to changelog.md for details and release history.
A comparison between v1.x and v2.0 codebases can be found in compatibility.md.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
ResNet
ResNeXt
VGG
HRNet
RegNet
Res2Net
Supported methods:
Some other methods are also supported in projects using MMDetection.
Installation
Please refer to get_started.md for installation.
Getting Started
Please refer to FAQ for frequently asked questions.
Contributing
We appreciate all contributions to improve MMDetection. 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}
}
Contact
This repo is currently maintained by Kai Chen (@hellock), Yuhang Cao (@yhcao6), Wenwei Zhang (@ZwwWayne),
Jiarui Xu (@xvjiarui). Other core developers include Jiangmiao Pang (@OceanPang) and Jiaqi Wang (@myownskyW7).