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</sup>
</div>
<div>&nbsp;</div>
</div>

[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/1.1/)
[![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)

</div>

</div>

<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218352562-cdded397-b0f3-4ca1-b8dd-a60df8dca75b.png" width="3%" alt="" /></a>
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<a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
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<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
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<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
</div>

**News**:

**v1.1.0rc3** was released in 7/1/2023
Expand Down Expand Up @@ -60,15 +77,15 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk

- **High efficiency**

It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/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 `×`.
It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/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 | × | × |
| 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.

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<li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
<li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
<li>DLA (CVPR'2018)</li>
<li>MinkResNet (CVPR'2019)</li>
</ul>
</td>
<td>
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<li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
<li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
</ul>
<li><b>Indoor</b></li>
<ul>
<li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
</ul>
</td>
<td>
<li><b>Outdoor</b></li>
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</tbody>
</table>

| | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| PGD | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| SA-SSD | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| PV-RCNN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
| | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet |
| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: |
| SECOND | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| PointPillars | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ |
| FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| VoteNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Part-A2 | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| MVXNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| CenterPoint | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| SSN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| ImVoteNet | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointNet++ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| DGCNN | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| SA-SSD | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| PV-RCNN | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |

**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.

## Installation

Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
Please refer to [get_started.md](docs/en/get_started.md) for installation.

## Get Started

Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/train_test.md) and [with new dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [customizing dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/train_test.md) and [with new dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [customizing dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).

Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/notes/compatibility.md) to be aware of the BC-breaking updates introduced in each version.

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