[Docs] Replace markdownlint with mdformat for avoiding installing ruby (#1489)

.github/CODE_OF_CONDUCT.md

@@ -14,22 +14,22 @@ appearance, race, religion, or sexual identity and orientation.
 Examples of behavior that contributes to creating a positive environment
 include:
 
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
 
 Examples of unacceptable behavior by participants include:
 
-* The use of sexualized language or imagery and unwelcome sexual attention or
- advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
- address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
 
 ## Our Responsibilities
 
@@ -70,7 +70,7 @@ members of the project's leadership.
 This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
 available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
 
-[homepage]: https://www.contributor-covenant.org
-
 For answers to common questions about this code of conduct, see
 https://www.contributor-covenant.org/faq
+
+[homepage]: https://www.contributor-covenant.org

.github/ISSUE_TEMPLATE/error-report.md

@@ -4,12 +4,12 @@ about: Create a report to help us improve
 title: ''
 labels: ''
 assignees: ''
-
 ---
 
 Thanks for your error report and we appreciate it a lot.
 
 **Checklist**
+
 1. I have searched related issues but cannot get the expected help.
 2. The bug has not been fixed in the latest version.
 
@@ -17,6 +17,7 @@ Thanks for your error report and we appreciate it a lot.
 A clear and concise description of what the bug is.
 
 **Reproduction**
+
 1. What command or script did you run?
 
 ```
@@ -30,8 +31,8 @@ A placeholder for the command.
 
 1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment information and paste it here.
 2. You may add addition that may be helpful for locating the problem, such as
-    - How you installed PyTorch [e.g., pip, conda, source]
-    - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+   - How you installed PyTorch \[e.g., pip, conda, source\]
+   - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
 
 **Error traceback**
 If applicable, paste the error trackback here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -4,15 +4,14 @@ about: Suggest an idea for this project
 title: ''
 labels: ''
 assignees: ''
-
 ---
 
 **Describe the feature**
 
 **Motivation**
 A clear and concise description of the motivation of the feature.
-Ex1. It is inconvenient when [....].
-Ex2. There is a recent paper [....], which is very helpful for [....].
+Ex1. It is inconvenient when \[....\].
+Ex2. There is a recent paper \[....\], which is very helpful for \[....\].
 
 **Related resources**
 If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.

.github/ISSUE_TEMPLATE/general_questions.md

@@ -4,5 +4,4 @@ about: Ask general questions to get help
 title: ''
 labels: ''
 assignees: ''
-
 ---

.github/ISSUE_TEMPLATE/reimplementation_questions.md

@@ -2,25 +2,27 @@
 name: Reimplementation Questions
 about: Ask about questions during model reimplementation
 title: ''
-labels: 'reimplementation'
+labels: reimplementation
 assignees: ''
-
 ---
 
 **Notice**
 
 There are several common situations in the reimplementation issues as below
+
 1. Reimplement a model in the model zoo using the provided configs
 2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets)
 3. Reimplement a custom model but all the components are implemented in MMDetection3D
 4. Reimplement a custom model with new modules implemented by yourself
 
 There are several things to do for different cases as below.
+
 - For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue.
 - For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write.
 - One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemted code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you.
 
 **Checklist**
+
 1. I have searched related issues but cannot get the expected help.
 2. The issue has not been fixed in the latest version.
 
@@ -29,6 +31,7 @@ There are several things to do for different cases as below.
 A clear and concise description of what the problem you meet and what have you done.
 
 **Reproduction**
+
 1. What command or script did you run?
 
 ```
@@ -48,8 +51,8 @@ A placeholder for the config.
 
 1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment information and paste it here.
 2. You may add addition that may be helpful for locating the problem, such as
-    - How you installed PyTorch [e.g., pip, conda, source]
-    - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+   - How you installed PyTorch \[e.g., pip, conda, source\]
+   - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
 
 **Results**
 

.github/pull_request_template.md

 Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
 
 ## Motivation
+
 Please describe the motivation of this PR and the goal you want to achieve through this PR.
 
 ## Modification
+
 Please briefly describe what modification is made in this PR.
 
 ## BC-breaking (Optional)
+
 Does the modification introduce changes that break the back-compatibility of the downstream repos?
 If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
 
 ## Use cases (Optional)
+
 If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
 
 ## Checklist

.pre-commit-config.yaml

 repos:
-  - repo: https://gitlab.com/pycqa/flake8.git
+  - repo: https://github.com/PyCQA/flake8
     rev: 3.8.3
     hooks:
       - id: flake8
@@ -24,16 +24,19 @@ repos:
         args: ["--remove"]
       - id: mixed-line-ending
         args: ["--fix=lf"]
-  - repo: https://github.com/markdownlint/markdownlint
-    rev: v0.11.0
-    hooks:
-      - id: markdownlint
-        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
-              "-t", "allow_different_nesting"]
   - repo: https://github.com/codespell-project/codespell
     rev: v2.1.0
     hooks:
       - id: codespell
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.14
+    hooks:
+      - id: mdformat
+        args: [ "--number" ]
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat_frontmatter
+          - linkify-it-py
   - repo: https://github.com/myint/docformatter
     rev: v1.3.1
     hooks:

README.md

@@ -24,7 +24,6 @@
 [![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 v1.0.0rc2.
 
 Note: We are going through large refactoring to provide simpler and more unified usage of many modules.
@@ -69,13 +68,13 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
   It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/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     |   ×      | ×    |
+  |       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.
 
@@ -214,28 +213,28 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
   </tbody>
 </table>
 
-|                    | ResNet   | ResNeXt  | SENet    |PointNet++ |DGCNN     | HRNet | RegNetX | Res2Net | DLA |
-|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|:---:|
-| SECOND             | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| PointPillars       | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| FreeAnchor         | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| VoteNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| H3DNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| 3DSSD            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| Part-A2            | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| MVXNet             | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| CenterPoint        | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| SSN                | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| ImVoteNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| FCOS3D               | ✓        | ☐        | ☐        | ✗         | ✗         | ☐     | ☐        | ☐     | ✗
-| PointNet++           | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| Group-Free-3D        | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| ImVoxelNet           | ✓         | ✗        | ✗        | ✗        | ✗         | ✗     | ✗        | ✗     | ✗
-| PAConv               | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| DGCNN                | ✗        | ✗        | ✗        | ✗         | ✓         | ✗     | ✗        | ✗     | ✗
-| SMOKE                | ✗        | ✗        | ✗        | ✗         | ✗         | ✗     | ✗        | ✗     | ✓
-| PGD                  | ✓        | ☐        | ☐        | ✗         | ✗         | ☐     | ☐        | ☐     | ✗
-| MonoFlex             | ✗        | ✗        | ✗        | ✗         | ✗         | ✗     | ✗        | ✗     | ✓
+|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA |
+| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: |
+| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
+| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |
+| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
+| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
+| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
 
 **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/en/model_zoo.md) can be trained or used in this codebase.
 
@@ -252,6 +251,7 @@ Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. When updat
 ## Model deployment
 
 Now MMDeploy has supported some MMDetection3D model deployment. Please refer to [model_deployment.md](docs/en/tutorials/model_deployment.md) for more details.
+
 ## Citation
 
 If you find this project useful in your research, please consider cite:

README_zh-CN.md

@@ -24,7 +24,6 @@
 [![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)
 
-
 **新闻**: 我们发布了版本 v1.0.0rc2.
 
 说明：我们正在进行大规模的重构，以提供对许多模块更简单、更统一的使用。
@@ -63,19 +62,19 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 - **与 2D 检测器的自然整合**
 
-   [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
+  [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
 
 - **性能高**
 
-   训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+  训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
 
-  | 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     |   ×      | ×    |
+  |       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                          |                           ×                            |                      ×                      |
 
 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样, MMDetection3D 也可以作为一个库去支持各式各样的项目.
 
@@ -214,29 +213,28 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
   </tbody>
 </table>
 
-|                    | ResNet   | ResNeXt  | SENet    |PointNet++ |DGCNN     | HRNet | RegNetX | Res2Net | DLA |
-|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|:---:|
-| SECOND             | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| PointPillars       | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| FreeAnchor         | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| VoteNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| H3DNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| 3DSSD            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| Part-A2            | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| MVXNet             | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| CenterPoint        | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| SSN                | ☐        | ☐        | ☐        | ✗         | ✗         | ☐     | ✓        | ☐     | ✗
-| ImVoteNet            | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| FCOS3D               | ✓        | ☐        | ☐        | ✗         | ✗         | ☐     | ☐        | ☐     | ✗
-| PointNet++           | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| Group-Free-3D        | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| ImVoxelNet           | ✓         | ✗        | ✗        | ✗        | ✗         | ✗     | ✗        | ✗     | ✗
-| PAConv               | ✗        | ✗        | ✗        | ✓         | ✗         | ✗     | ✗        | ✗     | ✗
-| DGCNN                | ✗        | ✗        | ✗        | ✗         | ✓         | ✗     | ✗        | ✗     | ✗
-| SMOKE                | ✗        | ✗        | ✗        | ✗         | ✗         | ✗     | ✗        | ✗     | ✓
-| PGD                  | ✓        | ☐        | ☐        | ✗         | ✗         | ☐     | ☐        | ☐     | ✗
-| MonoFlex             | ✗        | ✗        | ✗        | ✗         | ✗         | ✗     | ✗        | ✗     | ✓
-
+|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA |
+| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: |
+| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
+| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
+| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
+| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |
+| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
+| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
+| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
 
 **注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/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/3dssd/README.md

@@ -17,6 +17,7 @@ Currently, there have been many kinds of voxel-based 3D single stage detectors,
 We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
 
 Some settings in our implementation are different from the [official implementation](https://github.com/Jia-Research-Lab/3DSSD), which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
+
 1. We keep the scenes without any object while the official code skips these scenes in training. In the official implementation, only 3229 and 3394 samples are used as training and validation sets, respectively. In our implementation, we keep using 3712 and 3769 samples as training and validation sets, respectively, as those used for all the other models in our implementation on KITTI datasets.
 2. We do not modify the decay of `batch normalization` during training.
 3. While using [`DataBaseSampler`](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/dbsampler.py#L80) for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.
@@ -26,11 +27,11 @@ Some settings in our implementation are different from the [official implementat
 
 ### KITTI
 
-|  Backbone   |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP |Download |
-| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|    [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py)| Car |72e|4.7||78.58(81.27)<sup>1</sup>|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828.log.json)|
+|                   Backbone                    | Class | Lr schd | Mem (GB) | Inf time (fps) |           mAP            |                                                                                                                                                Download                                                                                                                                                |
+| :-------------------------------------------: | :---: | :-----: | :------: | :------------: | :----------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py) |  Car  |   72e   |   4.7    |                | 78.58(81.27)<sup>1</sup> | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828.log.json) |
 
-[1]: We report two different 3D object detection performance here. 78.58mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
+\[1\]: We report two different 3D object detection performance here. 78.58mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
 
 ## Citation
 

configs/centerpoint/README.md

@@ -108,23 +108,23 @@ data = dict(
 
 ### CenterPoint
 
-|Backbone|  Voxel type (voxel size)   |Dcn|Circular nms| Mem (GB) | Inf time (fps) | mAP |NDS| Download |
-| :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |:------: |
-|[SECFPN](./centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✗|✓|4.9| |56.19|64.43|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205-5db91e00.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205.log.json)|
-|above w/o circle nms|voxel (0.1)|✗|✗| | |56.56|64.46||
-|[SECFPN](./centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✓|✓|5.2| |56.34|64.81|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317-26d8176c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317.log.json)|
-|above w/o circle nms|voxel (0.1)|✓|✗| | |56.60|64.90||
-|[SECFPN](./centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✗|✓|7.8| |57.34|65.23|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905-358fbe3b.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905.log.json)|
-|above w/o circle nms|voxel (0.075)|✗|✗| | |57.63|65.39| |
-|[SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✓|✓|8.5| |57.27|65.58|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619-67c8496f.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619.log.json)|
-|above w/o circle nms|voxel (0.075)|✓|✗| | |57.43|65.63||
-|above w/ double flip|voxel (0.075)|✓|✗| | |59.73|67.39||
-|above w/ scale tta|voxel (0.075)|✓|✗| | |60.43|67.65||
-|above w/ circle nms w/o scale tta|voxel (0.075)|✓|✗| | |59.52|67.24||
-|[SECFPN](./centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|pillar (0.2)|✗|✓|4.4| |49.07|59.66|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716-a134a233.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716.log.json)|
-|above w/o circle nms|pillar (0.2)|✗|✗| | |49.12|59.66||
-|[SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|pillar (0.2)|✓|✗| 4.6| |48.8 |59.67 |[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722.log.json)|
-|above w/ circle nms|pillar (0.2)|✓|✓| | |48.79|59.65||
+|                                      Backbone                                       | Voxel type (voxel size) | Dcn | Circular nms | Mem (GB) | Inf time (fps) |  mAP  |  NDS  |                                                                                                                                                                                                                                                  Download                                                                                                                                                                                                                                                  |
+| :---------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    [SECFPN](./centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)    |       voxel (0.1)       |  ✗  |      ✓       |   4.9    |                | 56.19 | 64.43 |             [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205-5db91e00.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205.log.json)             |
+|                                above w/o circle nms                                 |       voxel (0.1)       |  ✗  |      ✗       |          |                | 56.56 | 64.46 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|  [SECFPN](./centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)  |       voxel (0.1)       |  ✓  |      ✓       |   5.2    |                | 56.34 | 64.81 |     [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317-26d8176c.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317.log.json)     |
+|                                above w/o circle nms                                 |       voxel (0.1)       |  ✓  |      ✗       |          |                | 56.60 | 64.90 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|   [SECFPN](./centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)   |      voxel (0.075)      |  ✗  |      ✓       |   7.8    |                | 57.34 | 65.23 |         [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905-358fbe3b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905.log.json)         |
+|                                above w/o circle nms                                 |      voxel (0.075)      |  ✗  |      ✗       |          |                | 57.63 | 65.39 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| [SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py) |      voxel (0.075)      |  ✓  |      ✓       |   8.5    |                | 57.27 | 65.58 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619-67c8496f.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619.log.json) |
+|                                above w/o circle nms                                 |      voxel (0.075)      |  ✓  |      ✗       |          |                | 57.43 | 65.63 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|                                above w/ double flip                                 |      voxel (0.075)      |  ✓  |      ✗       |          |                | 59.73 | 67.39 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|                                 above w/ scale tta                                  |      voxel (0.075)      |  ✓  |      ✗       |          |                | 60.43 | 67.65 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|                          above w/ circle nms w/o scale tta                          |      voxel (0.075)      |  ✓  |      ✗       |          |                | 59.52 | 67.24 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|   [SECFPN](./centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)    |      pillar (0.2)       |  ✗  |      ✓       |   4.4    |                | 49.07 | 59.66 |           [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716-a134a233.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716.log.json)           |
+|                                above w/o circle nms                                 |      pillar (0.2)       |  ✗  |      ✗       |          |                | 49.12 | 59.66 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|      [SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)       |      pillar (0.2)       |  ✓  |      ✗       |   4.6    |                | 48.8  | 59.67 |                       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722.log.json)                       |
+|                                 above w/ circle nms                                 |      pillar (0.2)       |  ✓  |      ✓       |          |                | 48.79 | 59.65 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
 
 ## Citation
 

configs/dgcnn/README.md

@@ -22,22 +22,22 @@ We implement DGCNN and provide the results and checkpoints on S3DIS dataset.
 
 ### S3DIS
 
-|                                   Method                                    | Split  |  Lr schd   | Mem (GB) | Inf time (fps) | mIoU (Val set) |         Download         |
-| :-------------------------------------------------------------------------: | :----: | :--------: | :------: | :------------: | :------------: | :----------------------: |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_1 | cosine 100e |   13.1    |                |     68.33      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_2 | cosine 100e |   13.1    |                |     40.68      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_3 | cosine 100e |   13.1    |                |     69.38      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_4 | cosine 100e |   13.1    |                |     50.07      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_5 | cosine 100e |   13.1    |                |     50.59      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_6 | cosine 100e |   13.1    |                |     77.94      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
-| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | 6-fold |           |           |                |     59.43      |        |
+|                          Method                           | Split  |   Lr schd   | Mem (GB) | Inf time (fps) | mIoU (Val set) |                                                                                                                                                                                                 Download                                                                                                                                                                                                 |
+| :-------------------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_1 | cosine 100e |   13.1   |                |     68.33      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_2 | cosine 100e |   13.1   |                |     40.68      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_3 | cosine 100e |   13.1   |                |     69.38      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_4 | cosine 100e |   13.1   |                |     50.07      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_5 | cosine 100e |   13.1   |                |     50.59      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_6 | cosine 100e |   13.1   |                |     77.94      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | 6-fold |             |          |                |     59.43      |                                                                                                                                                                                                                                                                                                                                                                                                          |
 
 **Notes:**
 
--   We use XYZ+Color+Normalized_XYZ as input in all the experiments on S3DIS datasets.
--   `Area_5` Split means training the model on Area_1, 2, 3, 4, 6 and testing on Area_5.
--   `6-fold` Split means the overall result of 6 different splits (Area_1, Area_2, Area_3, Area_4, Area_5 and Area_6 Splits).
--   Users need to modify `train_area` and `test_area` in the S3DIS dataset's [config](./configs/_base_/datasets/s3dis_seg-3d-13class.py) to set the training and testing areas, respectively.
+- We use XYZ+Color+Normalized_XYZ as input in all the experiments on S3DIS datasets.
+- `Area_5` Split means training the model on Area_1, 2, 3, 4, 6 and testing on Area_5.
+- `6-fold` Split means the overall result of 6 different splits (Area_1, Area_2, Area_3, Area_4, Area_5 and Area_6 Splits).
+- Users need to modify `train_area` and `test_area` in the S3DIS dataset's [config](./configs/_base_/datasets/s3dis_seg-3d-13class.py) to set the training and testing areas, respectively.
 
 ## Indeterminism
 

configs/dynamic_voxelization/README.md

@@ -20,11 +20,11 @@ We implement Dynamic Voxelization proposed in  and provide its results and model
 
 ### KITTI
 
-|  Model   |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
-| :---------: | :-----: |:-----: | :------: | :------------: | :----: | :------: |
-|[SECOND](./dv_second_secfpn_6x8_80e_kitti-3d-car.py)|Car    |cyclic 80e|5.5||78.83|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_second_secfpn_6x8_80e_kitti-3d-car/dv_second_secfpn_6x8_80e_kitti-3d-car_20200620_235228-ac2c1c0c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_second_secfpn_6x8_80e_kitti-3d-car/dv_second_secfpn_6x8_80e_kitti-3d-car_20200620_235228.log.json)|
-|[SECOND](./dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py)| 3 Class|cosine 80e|5.5||65.27|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/dynamic_voxelization/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class_20210831_054106-e742d163.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/dynamic_voxelization/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class_20210831_054106.log.json)|
-|[PointPillars](./dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py)| Car|cyclic 80e|4.7||77.76|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230844-ee7b75c9.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230844.log.json)|
+|                               Model                               |  Class  |  Lr schd   | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                                                                                 Download                                                                                                                                                                                                                 |
+| :---------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|       [SECOND](./dv_second_secfpn_6x8_80e_kitti-3d-car.py)        |   Car   | cyclic 80e |   5.5    |                | 78.83 |                     [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_second_secfpn_6x8_80e_kitti-3d-car/dv_second_secfpn_6x8_80e_kitti-3d-car_20200620_235228-ac2c1c0c.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_second_secfpn_6x8_80e_kitti-3d-car/dv_second_secfpn_6x8_80e_kitti-3d-car_20200620_235228.log.json)                     |
+|  [SECOND](./dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py)   | 3 Class | cosine 80e |   5.5    |                | 65.27 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/dynamic_voxelization/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class_20210831_054106-e742d163.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/dynamic_voxelization/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class_20210831_054106.log.json) |
+| [PointPillars](./dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py) |   Car   | cyclic 80e |   4.7    |                | 77.76 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230844-ee7b75c9.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/dynamic_voxelization/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230844.log.json)       |
 
 ## Citation
 

configs/fcos3d/README.md

@@ -50,11 +50,11 @@ We also provide visualization functions to show the monocular 3D detection resul
 
 ### NuScenes
 
-|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
-| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|[ResNet101 w/ DCN](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py)|1x|8.69||29.8|37.7|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json)|
-|[above w/ finetune](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py)|1x|8.69||32.1|39.5|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645.log.json)|
-|above w/ tta|1x|8.69||33.1|40.3||
+|                                        Backbone                                        | Lr schd | Mem (GB) | Inf time (fps) | mAP  | NDS  |                                                                                                                                                                                                                             Download                                                                                                                                                                                                                             |
+| :------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|      [ResNet101 w/ DCN](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py)      |   1x    |   8.69   |                | 29.8 | 37.7 |                   [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json)                   |
+| [above w/ finetune](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py) |   1x    |   8.69   |                | 32.1 | 39.5 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645.log.json) |
+|                                      above w/ tta                                      |   1x    |   8.69   |                | 33.1 | 40.3 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 
 ## Citation
 

configs/free_anchor/README.md

@@ -80,16 +80,16 @@ model = dict(
 
 ### PointPillars
 
-|  Backbone   |FreeAnchor|Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
-| :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |
-|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.1||40.0|53.3|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
-|[FPN](./hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|16.3||43.82|54.86|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441.log.json)|
-|[RegNetX-400MF-FPN](../regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.3||44.8|56.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)|
-|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|17.6||48.3|58.65|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_213939-a2dd3fff.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_213939.log.json)|
-|[RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|24.3||52.04|61.49|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608-bfbd506e.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608.log.json)|
-|[RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)*|✓|3x|24.4||52.69|62.45|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210827_184909-14d2dbd1.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210827_184909.log.json)|
-|[RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|29.4||52.4|61.94|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237-e385c35a.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237.log.json)|
-|[RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)*|✓|3x|29.2||54.23|63.41|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210828_030816-06708918.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210828_030816.log.json)|
+|                                                 Backbone                                                  | FreeAnchor | Lr schd | Mem (GB) | Inf time (fps) |  mAP  |  NDS  |                                                                                                                                                                                                                                                                    Download                                                                                                                                                                                                                                                                    |
+| :-------------------------------------------------------------------------------------------------------: | :--------: | :-----: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|                    [FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)                    |     ✗      |   2x    |   17.1   |                | 40.0  | 53.3  |                                                                        [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)                                                                        |
+|                     [FPN](./hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)                     |     ✓      |   2x    |   16.3   |                | 43.82 | 54.86 |                                                 [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441.log.json)                                                 |
+|         [RegNetX-400MF-FPN](../regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)          |     ✗      |   2x    |   17.3   |                | 44.8  | 56.4  |                                                    [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)                                                    |
+|       [RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)        |     ✓      |   2x    |   17.6   |                | 48.3  | 58.65 |                       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_213939-a2dd3fff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_213939.log.json)                       |
+|       [RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)        |     ✓      |   2x    |   24.3   |                | 52.04 | 61.49 |                       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608-bfbd506e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608.log.json)                       |
+| [RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)\* |     ✓      |   3x    |   24.4   |                | 52.69 | 62.45 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210827_184909-14d2dbd1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210827_184909.log.json) |
+|       [RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)        |     ✓      |   2x    |   29.4   |                | 52.4  | 61.94 |                       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237-e385c35a.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237.log.json)                       |
+| [RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)\* |     ✓      |   3x    |   29.2   |                | 54.23 | 63.41 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210828_030816-06708918.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210828_030816.log.json) |
 
 **Note**: Models noted by `*` means it is trained using stronger augmentation with vertical flip under bird-eye-view, global translation, and larger range of global rotation.
 

configs/groupfree3d/README.md

@@ -20,12 +20,12 @@ We implement Group-Free-3D and provide the result and checkpoints on ScanNet dat
 
 ### ScanNet
 
-|  Method  |  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
-| :------: | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-| [L6, O256](./groupfree3d_8x4_scannet-3d-18class-L6-O256.py ) |    PointNet++     |  3x    |6.7||66.32 (65.67*)|47.82 (47.74*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347-3499eb55.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347.log.json)|
-| [L12, O256](./groupfree3d_8x4_scannet-3d-18class-L12-O256.py ) |    PointNet++     |  3x    |9.4||66.57 (66.22*)|48.21 (48.95*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907-1c5551ad.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907.log.json)|
-| [L12, O256](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256.py ) |    PointNet++w2x     |  3x    |13.3||68.20 (67.30*)|51.02 (50.44*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301-944f0ac0.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301.log.json)|
-| [L12, O512](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512.py ) |    PointNet++w2x     |  3x    |18.8||68.22 (68.20*)|52.61 (51.31*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204-187b71c7.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204.log.json)|
+|                              Method                               |   Backbone    | Lr schd | Mem (GB) | Inf time (fps) |     AP@0.25     |     AP@0.5      |                                                                                                                                                                                                        Download                                                                                                                                                                                                        |
+| :---------------------------------------------------------------: | :-----------: | :-----: | :------: | :------------: | :-------------: | :-------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    [L6, O256](./groupfree3d_8x4_scannet-3d-18class-L6-O256.py)    |  PointNet++   |   3x    |   6.7    |                | 66.32 (65.67\*) | 47.82 (47.74\*) |           [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347-3499eb55.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347.log.json)           |
+|   [L12, O256](./groupfree3d_8x4_scannet-3d-18class-L12-O256.py)   |  PointNet++   |   3x    |   9.4    |                | 66.57 (66.22\*) | 48.21 (48.95\*) |         [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907-1c5551ad.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907.log.json)         |
+| [L12, O256](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256.py) | PointNet++w2x |   3x    |   13.3   |                | 68.20 (67.30\*) | 51.02 (50.44\*) | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301-944f0ac0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301.log.json) |
+| [L12, O512](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512.py) | PointNet++w2x |   3x    |   18.8   |                | 68.22 (68.20\*) | 52.61 (51.31\*) | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204-187b71c7.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204.log.json) |
 
 **Notes:**
 

configs/h3dnet/README.md

@@ -20,11 +20,11 @@ We implement H3DNet and provide the result and checkpoints on ScanNet datasets.
 
 ### ScanNet
 
-|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
-| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|    [MultiBackbone](./h3dnet_3x8_scannet-3d-18class.py)     |  3x    |7.9||66.07|47.68|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_3x8_scannet-3d-18class_20210824_003149-414bd304.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_3x8_scannet-3d-18class_20210824_003149.log.json) |
+|                      Backbone                       | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 |                                                                                                                                                           Download                                                                                                                                                           |
+| :-------------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [MultiBackbone](./h3dnet_3x8_scannet-3d-18class.py) |   3x    |   7.9    |                |  66.07  | 47.68  | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_3x8_scannet-3d-18class_20210824_003149-414bd304.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_3x8_scannet-3d-18class_20210824_003149.log.json) |
 
-**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_h3dnet_checkpoints.py](../../tools/model_converters/convert_h3dnet_checkpoints.py):
+**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version \< 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_h3dnet_checkpoints.py](../../tools/model_converters/convert_h3dnet_checkpoints.py):
 
 ```
 python ./tools/model_converters/convert_h3dnet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}

configs/imvotenet/README.md

@@ -20,15 +20,15 @@ We implement ImVoteNet and provide the result and checkpoints on SUNRGBD.
 
 ### SUNRGBD-2D (Stage 1, image branch pre-train)
 
-|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
-| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|    [PointNet++](./imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class.py)     |   |2.1| ||62.70|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210819_225618-62eba6ce.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210819_225618.json)|
+|                                Backbone                                 | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 |                                                                                                                                                                                                              Download                                                                                                                                                                                                              |
+| :---------------------------------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [PointNet++](./imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class.py) |         |   2.1    |                |         | 62.70  | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210819_225618-62eba6ce.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210819_225618.json) |
 
 ### SUNRGBD-3D (Stage 2)
 
-|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
-| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|    [PointNet++](./imvotenet_stage2_16x8_sunrgbd-3d-10class.py)     |  3x    |9.4| |64.55||[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210819_192851-1bcd1b97.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210819_192851.log.json)|
+|                          Backbone                           | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 |                                                                                                                                                                                        Download                                                                                                                                                                                        |
+| :---------------------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [PointNet++](./imvotenet_stage2_16x8_sunrgbd-3d-10class.py) |   3x    |   9.4    |                |  64.55  |        | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210819_192851-1bcd1b97.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210819_192851.log.json) |
 
 ## Citation
 

configs/imvoxelnet/README.md

@@ -22,9 +22,9 @@ Results for SUN RGB-D, ScanNet and nuScenes are currently available in ImVoxelNe
 
 ### KITTI
 
-|  Backbone   |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
-| :---------: | :-----: |:-----: | :------: | :------------: | :----: |:----: |
-| [ResNet-50](./imvoxelnet_kitti-3d-car.py) | Car | 3x | | |17.26|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014-3d0ffdf4.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014.log.json)|
+|                 Backbone                  | Class | Lr schd | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                               Download                                                                                                                                                               |
+| :---------------------------------------: | :---: | :-----: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [ResNet-50](./imvoxelnet_kitti-3d-car.py) |  Car  |   3x    |          |                | 17.26 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014-3d0ffdf4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014.log.json) |
 
 ## Citation
 

configs/monoflex/README.md

@@ -20,17 +20,17 @@ We implement MonoFlex and provide the results and checkpoints on KITTI dataset.
 
 ### KITTI
 
-|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
-| :---------: | :-----: | :------: | :------------: | :----: | :------: |
-|[DLA34](./monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d.py)|6x|9.64||21.86|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/monoflex/monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d_20211228_027553-d46d9bb0.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/monoflex/monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d_20211228_027553.log.json)
+|                                Backbone                                 | Lr schd | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                               Download                                                                                                                                                               |
+| :---------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [DLA34](./monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d.py) |   6x    |   9.64   |                | 21.86 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/monoflex/monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d_20211228_027553-d46d9bb0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/monoflex/monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d_20211228_027553.log.json) |
 
 Note: mAP represents Car moderate 3D strict AP11 results.
 Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 and AP40 metric:
 
-|             |     Easy      |    Moderate    |     Hard      |
-|-------------|:-------------:|:--------------:|:-------------:|
-| Car (AP11)  | 28.02 / 36.11 | 21.86 / 29.46  | 19.01 / 24.83 |
-| Car (AP40)  | 23.22 / 32.74 | 17.18 / 24.02  | 15.13 / 20.67 |
+|            |     Easy      |   Moderate    |     Hard      |
+| ---------- | :-----------: | :-----------: | :-----------: |
+| Car (AP11) | 28.02 / 36.11 | 21.86 / 29.46 | 19.01 / 24.83 |
+| Car (AP40) | 23.22 / 32.74 | 17.18 / 24.02 | 15.13 / 20.67 |
 
 Note: mAP represents Car moderate 3D strict AP11 / AP40 results. Because of the limited data for pedestrians and cyclists, the detection performance for these two classes is usually unstable. Therefore, we only list car detection results here. In addition, the AP11 result may fluctuate in a larger range (~1 AP), so AP40 is a more recommended metric for reference due to its much better stability.