News: We released the codebase v0.1.0.
Documentation: https://mmdetection3d.readthedocs.io/
The master branch works with PyTorch 1.3 to 1.5.
MMDetection3D is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project developed by MMLab.
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Modular Design
We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.
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Support of multiple frameworks out of box
The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.
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High efficiency
The training speed is faster than other codebases.
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State of the art
The accuracy of models is faster than other codebases.
Apart from MMDetection3D, we also released a library MMDetection and mmcv for computer vision research, which are heavily depended on by this toolbox.
This project is released under the Apache 2.0 license.
v0.1.0 was released in 24/6/2020. Please refer to changelog.md for details and release history.
Supported methods and backbones are shown in the below table. Results and models are available in the model zoo.
| ResNet | ResNeXt | SENet | PointNet++ | HRNet | RegNetX | Res2Net | |
|---|---|---|---|---|---|---|---|
| SECOND | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ |
Other features
Notice: All the models or modules supported in MMDetection's model zoo can be trained or used in this codebase.
Please refer to install.md for installation and dataset preparation.
Please see getting_started.md for the basic usage of MMDetection. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.
We appreciate all contributions to improve MMDetection. Please refer to CONTRIBUTING.md for the contributing guideline.
MMDetection3D 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 3D detectors.
If you use this toolbox or benchmark in your research, please cite this project.
@misc{mmdetection3d_2020,
title = {{MMDetection3D}},
author = {Zhang, Wenwei and Wu, Yuefeng and Li, Yinhao and Lin, Kwan-Yee and
Qian, Chen, Shi, Jianping, and Chen, Kai, and Li, Hongsheng and
Lin, Dahua, and Loy, Chen Change},
howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
year = {2020}
}
This repo is currently maintained by Wenwei Zhang (@ZwwWayne).