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Unverified Commit b481efcc authored by Sun Jiahao's avatar Sun Jiahao Committed by GitHub
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[Docs] Add docs and README for MinkUnet (#2358) 

* add readme

* rename

* fix miou typo

* add link

* fix backbone name

* add torchsparse link

* revise link
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README.md
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...@@ -134,6 +134,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md). ...@@ -134,6 +134,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
<li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li> <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
<li>DLA (CVPR'2018)</li> <li>DLA (CVPR'2018)</li>
<li>MinkResNet (CVPR'2019)</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> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
</ul> </ul>
</td> </td>
...@@ -221,6 +222,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md). ...@@ -221,6 +222,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
<td> <td>
<li><b>Outdoor</b></li> <li><b>Outdoor</b></li>
<ul> <ul>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
</ul> </ul>
<li><b>Indoor</b></li> <li><b>Indoor</b></li>
...@@ -237,32 +239,33 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md). ...@@ -237,32 +239,33 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
</tbody> </tbody>
</table> </table>
| | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | | | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet |
| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | | :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | :------: |
| SECOND | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | SECOND | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointPillars | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | | PointPillars | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | | FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| VoteNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | VoteNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | H3DNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | 3DSSD | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Part-A2 | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | Part-A2 | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MVXNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | MVXNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterPoint | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | CenterPoint | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SSN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | | SSN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| ImVoteNet | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | ImVoteNet | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointNet++ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PointNet++ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | Group-Free-3D | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PAConv | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| DGCNN | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | | DGCNN | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | | SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | | MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| SA-SSD | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | SA-SSD | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | | FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| PV-RCNN | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | PV-RCNN | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | | Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
**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. **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.
......
README_zh-CN.md
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...@@ -131,6 +131,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代 ...@@ -131,6 +131,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
<li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li> <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
<li>DLA (CVPR'2018)</li> <li>DLA (CVPR'2018)</li>
<li>MinkResNet (CVPR'2019)</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> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
</ul> </ul>
</td> </td>
...@@ -217,6 +218,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代 ...@@ -217,6 +218,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
<td> <td>
<li><b>室外</b></li> <li><b>室外</b></li>
<ul> <ul>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li> <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
</ul> </ul>
<li><b>室内</b></li> <li><b>室内</b></li>
...@@ -233,32 +235,33 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代 ...@@ -233,32 +235,33 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
</tbody> </tbody>
</table> </table>
| | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | | | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet |
| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | | :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | :------: |
| SECOND | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | SECOND | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointPillars | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | | PointPillars | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | | FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| VoteNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | VoteNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | H3DNet | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | 3DSSD | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Part-A2 | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | Part-A2 | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MVXNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | MVXNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterPoint | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | CenterPoint | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SSN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | | SSN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| ImVoteNet | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | ImVoteNet | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointNet++ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PointNet++ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | Group-Free-3D | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PAConv | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| DGCNN | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | | DGCNN | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | | SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | | PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | | MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| SA-SSD | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | SA-SSD | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | | FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| PV-RCNN | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | | PV-RCNN | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | | Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
**注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。 **注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
......
configs/minkunet/README.md 0 → 100644
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# 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
> [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://arxiv.org/abs/1904.08755)
<!-- [ALGORITHM] -->
## Abstract
In many robotics and VR/AR applications, 3D-videos are readily-available sources of input (a continuous sequence of depth images, or LIDAR scans). However, those 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose the generalized sparse convolution that encompasses all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that convolutional neural networks with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise, outperform 3D convolutional neural networks and are faster than the 3D counterpart in some cases.
<div align=center>
<img src="https://user-images.githubusercontent.com/72679458/225243534-cd0ed738-4224-4e7c-bcac-4f4c8d89f3a9.png" width="800"/>
</div>
## Introduction
We implement MinkUNet with [TorchSparse](https://github.com/mit-han-lab/torchsparse) backend and provide the result and checkpoints on SemanticKITTI datasets.
## Results and models
### SemanticKITTI
| Method | Lr schd | Mem (GB) | mIoU | Download |
| :----------: | :-----: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| MinkUNet-W16 | 15e | 3.4 | 60.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737.log) |
| MinkUNet-W20 | 15e | 3.7 | 61.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718.log) |
| MinkUNet-W32 | 15e | 4.9 | 63.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710.log) |
**Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
**Note:** Due to TorchSparse backend, the model performance is unstable with TorchSparse backend and may fluctuate by about 1.5 mIoU for different random seeds.
## Citation
```latex
@inproceedings{choy20194d,
title={4d spatio-temporal convnets: Minkowski convolutional neural networks},
author={Choy, Christopher and Gwak, JunYoung and Savarese, Silvio},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={3075--3084},
year={2019}
}
```
configs/minkunet/metafile.yml 0 → 100644
View file @ b481efcc
Collections:
- Name: MinkUNet
Metadata:
Training Techniques:
- AdamW
Architecture:
- MinkUNet
Paper:
URL: https://arxiv.org/abs/1904.08755
Title: '4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks'
README: configs/minkunet/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection3d/blob/1.1/mmdet3d/models/segmentors/minkunet.py#L13
Version: v1.1.0rc4
Models:
- Name: minkunet_w16_8xb2-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w16_8xb2-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 3.4
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 60.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth
- Name: minkunet_w20_8xb2-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w20_8xb2-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 3.7
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 61.6
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth
- Name: minkunet_w32_8xb2-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w32_8xb2-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 4.9
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 63.1
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth
model-index.yml
View file @ b481efcc
...@@ -23,6 +23,7 @@ Import: ...@@ -23,6 +23,7 @@ Import:
- configs/smoke/metafile.yml - configs/smoke/metafile.yml
- configs/ssn/metafile.yml - configs/ssn/metafile.yml
- configs/votenet/metafile.yml - configs/votenet/metafile.yml
- configs/minkunet/metafile.yml
- configs/cylinder3d/metafile.yml - configs/cylinder3d/metafile.yml
- configs/pv_rcnn/metafile.yml - configs/pv_rcnn/metafile.yml
- configs/fcaf3d/metafile.yml - configs/fcaf3d/metafile.yml
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