Metadata-Version: 2.1
Name: mmdet3d
Version: 0.13.0
Summary: OpenMMLab's next-generation platformfor general 3D object detection.
Home-page: https://github.com/open-mmlab/mmdetection3d
Author: OpenMMLab
Author-email: zwwdev@gmail.com
License: Apache License 2.0
Description: <div align="center">
          <img src="resources/mmdet3d-logo.png" width="600"/>
        </div>
        
        [![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
        [![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
        [![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
        [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
        
        
        **News**: We released the codebase v0.13.0.
        
        In the recent [nuScenes 3D detection challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
        
        Code and models for the best vision-only method, [FCOS3D](https://arxiv.org/abs/2104.10956), have been released. Please stay tuned for [MoCa](https://arxiv.org/abs/2012.12741).
        
        Documentation: https://mmdetection3d.readthedocs.io/
        
        ## Introduction
        
        English | [简体中文](README_zh-CN.md)
        
        The master branch works with **PyTorch 1.3+**.
        
        MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is
        a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk/).
        
        ![demo image](resources/mmdet3d_outdoor_demo.gif)
        
        ### Major features
        
        - **Support multi-modality/single-modality detectors out of box**
        
          It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.
        
        - **Support indoor/outdoor 3D detection out of box**
        
          It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.
          For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages).
        
        - **Natural integration with 2D detection**
        
          All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
        
        - **High efficiency**
        
          It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
        
          | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) |[votenet](https://github.com/facebookresearch/votenet)| [Det3D](https://github.com/poodarchu/Det3D) |
          |:-------:|:-------------:|:---------:|:-----:|:-----:|
          | VoteNet | 358           | ×         |   77  | ×     |
          | PointPillars-car| 141           | ×         |   ×  | 140     |
          | PointPillars-3class| 107           |44     |   ×      | ×    |
          | SECOND| 40           |30     |   ×      | ×    |
          | Part-A2| 17           |14     |   ×      | ×    |
        
        Like [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMCV](https://github.com/open-mmlab/mmcv), MMDetection3D can also be used as a library to support different projects on top of it.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Changelog
        
        v0.13.0 was released in 1/5/2021.
        Please refer to [changelog.md](docs/changelog.md) for details and release history.
        
        ## Benchmark and model zoo
        
        Supported methods and backbones are shown in the below table.
        Results and models are available in the [model zoo](docs/model_zoo.md).
        
        Support backbones:
        
        - [x] PointNet (CVPR'2017)
        - [x] PointNet++ (NeurIPS'2017)
        - [x] RegNet (CVPR'2020)
        
        Support methods
        
        - [x] [SECOND (Sensor'2018)](configs/second/README.md)
        - [x] [PointPillars (CVPR'2019)](configs/pointpillars/README.md)
        - [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor/README.md)
        - [x] [VoteNet (ICCV'2019)](configs/votenet/README.md)
        - [x] [H3DNet (ECCV'2020)](configs/h3dnet/README.md)
        - [x] [3DSSD (CVPR'2020)](configs/3dssd/README.md)
        - [x] [Part-A2 (TPAMI'2020)](configs/parta2/README.md)
        - [x] [MVXNet (ICRA'2019)](configs/mvxnet/README.md)
        - [x] [CenterPoint (CVPR'2021)](configs/centerpoint/README.md)
        - [x] [SSN (ECCV'2020)](configs/ssn/README.md)
        - [x] [ImVoteNet (CVPR'2020)](configs/imvotenet/README.md)
        - [x] [FCOS3D (Arxiv'2021)](configs/fcos3d/README.md)
        
        |                    | ResNet   | ResNeXt  | SENet    |PointNet++ | HRNet | RegNetX | Res2Net |
        |--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:|
        | SECOND             | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | PointPillars       | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | FreeAnchor         | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | VoteNet            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
        | H3DNet            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
        | 3DSSD            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
        | Part-A2            | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | MVXNet             | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | CenterPoint        | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | SSN                | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
        | ImVoteNet            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
        | FCOS3D               | ✓        | ☐        | ☐        | ✗         | ☐     | ☐        | ☐     |
        
        Other features
        - [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
        
        **Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
        
        ## Installation
        
        Please refer to [getting_started.md](docs/getting_started.md) for installation.
        
        ## Get Started
        
        Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with customized dataset](docs/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/tutorials/config.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [Waymo dataset](docs/tutorials/waymo.md).
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```latex
        @misc{mmdet3d2020,
            title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
            author={MMDetection3D Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
            year={2020}
        }
        ```
        
        ## Contributing
        
        We appreciate all contributions to improve MMDetection3D. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
        
        ## Acknowledgement
        
        MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors 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.
        
        ## Projects in OpenMMLab
        
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab next-generation platform for general 3D object detection.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
        
Keywords: computer vision,3D object detection
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: optional
