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mm3dtest
gntmky/mm3dtest
mm3dtest is a machine learning model from gntmky. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
<div align="center" <img src="resources/mmdet3d-logo.png" width="600"/ <div </div <div align="center" <b<font size="5"OpenMMLab website</font</b <sup <a href="https://openmmlab.com" <i<font size="4"HOT</font</i <…
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- Created
- Jun 4, 2024
- Updated
- Jun 5, 2024
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Model card
From the Hugging Face model README
<div align="center">
<img src="resources/mmdet3d-logo.png" width="600"/>
<div> </div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div> </div>
📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🚀Ongoing Projects | 🤔Reporting Issues
</div> <div align="center">English | 简体中文
</div> <div align="center"> <a href="https://openmmlab.medium.com/" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <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> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <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> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a> </div>Introduction
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.
The main branch works with PyTorch 1.8+.
-
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.
-
Natural integration with 2D detection
All the about 300+ models, methods of 40+ papers, and modules supported in MMDetection 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. 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
✗.
Like MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it.
What's New
Highlight
In version 1.4, MMDetecion3D refactors the Waymo dataset and accelerates the preprocessing, training/testing setup, and evaluation of Waymo dataset. We also extends the support for camera-based, such as Monocular and BEV, 3D object detection models on Waymo. A detailed description of the Waymo data information is provided here.
Besides, in version 1.4, MMDetection3D provides Waymo-mini to help community users get started with Waymo and use it for quick iterative development.
v1.4.0 was released in 8/1/2024：
- Support the training of DSVT in
projects - Support Nerf-Det in
projects - Refactor Waymo dataset
v1.3.0 was released in 18/10/2023:
- Support CENet in
projects - Enhance demos with new 3D inferencers
v1.2.0 was released in 4/7/2023
- Support New Config Type in
mmdet3d/configs - Support the inference of DSVT in
projects - Support downloading datasets from OpenDataLab using
mim
v1.1.1 was released in 30/5/2023:
- Support TPVFormer in
projects - Support the training of BEVFusion in
projects - Support lidar-based 3D semantic segmentation benchmark
Installation
Please refer to Installation for installation instructions.
Getting Started
For detailed user guides and advanced guides, please refer to our documentation:
<details> <summary>User Guides</summary> </details> <details> <summary>Advanced Guides</summary> </details>Overview of Benchmark and Model Zoo
Results and models are available in the model zoo.
<div align="center"> <b>Components</b> </div> <table align="center"> <tbody> <tr align="center" valign="bottom"> <td> <b>Backbones</b> </td> <td> <b>Heads</b> </td> <td> <b>Features</b> </td> </tr> <tr valign="top"> <td> <ul> <li><a href="configs/pointnet2">PointNet (CVPR'2017)</a></li> <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li> <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> <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li> </ul> </td> <td> <ul> <li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li> </ul> </td> <td> <ul> <li><a href="configs/dynamic_voxelization">Dynamic Voxelization (CoRL'2019)</a></li> </ul> </td> </tr> </td> </tr> </tbody> </table> <div align="center"> <b>Architectures</b> </div> <table align="center"> <tbody> <tr align="center" valign="middle"> <td> <b>LiDAR-based 3D Object Detection</b> </td> <td> <b>Camera-based 3D Object Detection</b> </td> <td> <b>Multi-modal 3D Object Detection</b> </td> <td> <b>3D Semantic Segmentation</b> </td> </tr> <tr valign="top"> <td> <li><b>Outdoor</b></li> <ul> <li><a href="configs/second">SECOND (Sensor'2018)</a></li> <li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li> <li><a href="configs/ssn">SSN (ECCV'2020)</a></li> <li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li> <li><a href="configs/sassd">SA-SSD (CVPR'2020)</a></li> <li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li> <li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li> <li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li> <li><a href="configs/pv_rcnn">PV-RCNN (CVPR'2020)</a></li> <li><a href="projects/CenterFormer">CenterFormer (ECCV'2022)</a></li> </ul> <li><b>Indoor</b></li> <ul> <li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li> <li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li> <li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li> <li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li> <li><a href="projects/TR3D">TR3D (ArXiv'2023)</a></li> </ul> </td> <td> <li><b>Outdoor</b></li> <ul> <li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li> <li><a href="configs/smoke">SMOKE (CVPRW'2020)</a></li> <li><a href="configs/fcos3d">FCOS3D (ICCVW'2021)</a></li> <li><a href="configs/pgd">PGD (CoRL'2021)</a></li> <li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li> <li><a href="projects/DETR3D">DETR3D (CoRL'2021)</a></li> <li><a href="projects/PETR">PETR (ECCV'2022)</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> <ul> <li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li> <li><a href="projects/BEVFusion">BEVFusion (ICRA'2023)</a></li> </ul> <li><b>Indoor</b></li> <ul> <li><a href="configs/imvotenet">ImVoteNet (CVPR'2020)</a></li> </ul> </td> <td> <li><b>Outdoor</b></li> <ul> <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li> <li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li> <li><a href="projects/TPVFormer">TPVFormer (CVPR'2023)</a></li> </ul> <li><b>Indoor</b></li> <ul> <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li> <li><a href="configs/paconv">PAConv (CVPR'2021)</a></li> <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li> </ul> </ul> </td> </tr> </td> </tr> </tbody> </table>| ResNet | VoVNet | Swin-T | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| SPVCNN | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| BEVFusion | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterFormer | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TR3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| DETR3D | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PETR | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TPVFormer | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
Note: All the about 500+ models, methods of 90+ papers in 2D detection supported by MMDetection can be trained or used in this codebase.
FAQ
Please refer to FAQ for frequently asked questions.
Contributing
We appreciate all contributions to improve MMDetection3D. Please refer to 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.
Citation
If you find this project useful in your research, please consider cite:
@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}
}
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.
- MMEval: A unified evaluation library for multiple machine learning libraries.
- MIM: MIM installs OpenMMLab packages.
- MMPreTrain: OpenMMLab pre-training 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.
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- Created
- Jun 4, 2024
- Updated
- Jun 5, 2024