[Docs] Refine README (#2207)

README.md

@@ -140,13 +140,13 @@ MMPose v1.0.0 is a major update, including many API and config file changes. Cur
 | DeepPose (CVPR 2014)              |    done     |
 | RLE (ICCV 2021)                   |    done     |
 | SoftWingloss (TIP 2021)           |             |
-| VideoPose3D (CVPR 2019)           |             |
+| VideoPose3D (CVPR 2019)           | in progress |
 | Hourglass (ECCV 2016)             |    done     |
 | LiteHRNet (CVPR 2021)             |    done     |
 | AdaptiveWingloss (ICCV 2019)      |    done     |
 | SimpleBaseline2D (ECCV 2018)      |    done     |
 | PoseWarper (NeurIPS 2019)         |             |
-| SimpleBaseline3D (ICCV 2017)      |             |
+| SimpleBaseline3D (ICCV 2017)      | in progress |
 | HMR (CVPR 2018)                   |             |
 | UDP (CVPR 2020)                   |    done     |
 | VIPNAS (CVPR 2021)                |    done     |

README_CN.md

@@ -138,13 +138,13 @@ MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的
 | DeepPose (CVPR 2014)              |    done     |
 | RLE (ICCV 2021)                   |    done     |
 | SoftWingloss (TIP 2021)           |             |
-| VideoPose3D (CVPR 2019)           |             |
+| VideoPose3D (CVPR 2019)           | in progress |
 | Hourglass (ECCV 2016)             |    done     |
 | LiteHRNet (CVPR 2021)             |    done     |
 | AdaptiveWingloss (ICCV 2019)      |    done     |
 | SimpleBaseline2D (ECCV 2018)      |    done     |
 | PoseWarper (NeurIPS 2019)         |             |
-| SimpleBaseline3D (ICCV 2017)      |             |
+| SimpleBaseline3D (ICCV 2017)      | in progress |
 | HMR (CVPR 2018)                   |             |
 | UDP (CVPR 2020)                   |    done     |
 | VIPNAS (CVPR 2021)                |    done     |

projects/README.md

@@ -48,4 +48,10 @@ We also provide some documentation listed below to help you get started:
   <img src="https://user-images.githubusercontent.com/26127467/226655503-3cee746e-6e42-40be-82ae-6e7cae2a4c7e.jpg" width="800" style="width: 800px; height: 200px; object-fit: cover"/>
   </div><br/>
 
+- **[📖Awesome MMPose](./awesome-mmpose/)**: A list of Tutorials, Papers, Datasets related to MMPose
+
+  <div align=center>
+  <img src="https://user-images.githubusercontent.com/13503330/231416285-5467d313-0732-4ada-97e1-12be6ec69a28.png" width="800"/>
+  </div><br/>
+
 - **What's next? Join the rank of <span style="color:blue"> *MMPose contributors* </span> by creating a new  project**!

projects/awesome-mmpose/README.md

@@ -0,0 +1,35 @@
+# Awesome MMPose
+
+A list of resources related to MMPose. Feel free to contribute!
+
+## Contents
+
+- [Tutorials](#tutorials)
+- [Papers](#papers)
+- [Datasets](#datasets)
+- [Projects](#projects)
+
+## Tutorials
+
+- [MMPose Tutorial (Chinese)](https://github.com/TommyZihao/MMPose_Tutorials)
+  MMPose 中文 Jupyter 教程，from 同济子豪兄
+- [OpenMMLab Course](https://github.com/open-mmlab/OpenMMLabCourse)
+  This repository hosts articles, lectures and tutorials on computer vision and OpenMMLab, helping learners to understand algorithms and master our toolboxes in a systematical way.
+
+## Papers
+
+- [\[paper\]](https://arxiv.org/abs/2207.10387) [\[code\]](https://github.com/luminxu/Pose-for-Everything) ECCV 2022, Pose for Everything: Towards Category-Agnostic Pose Estimation
+- [\[paper\]](https://arxiv.org/abs/2201.04676) [\[code\]](https://github.com/Sense-X/UniFormer) ICLR 2022, UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning
+- [\[paper\]](https://arxiv.org/abs/2201.07412) [\[code\]](https://github.com/aim-uofa/Poseur) ECCV 2022, Poseur:Direct Human Pose Regression with Transformers
+- [\[paper\]](https://arxiv.org/abs/2106.03348) [\[code\]](https://github.com/ViTAE-Transformer/ViTAE-Transformer) NeurIPS 2022, ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond
+- [\[paper\]](https://arxiv.org/abs/2204.10762) [\[code\]](https://github.com/ZiyiZhang27/Dite-HRNet) IJCAI-ECAI 2021, Dite-HRNet:Dynamic Lightweight High-Resolution Network for Human Pose Estimation
+- [\[paper\]](https://arxiv.org/abs/2302.08453) [\[code\]](https://github.com/TencentARC/T2I-Adapter) T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
+- [\[paper\]](https://arxiv.org/pdf/2303.11638.pdf) [\[code\]](https://github.com/Gengzigang/PCT) CVPR 2023, Human Pose as Compositional Tokens
+
+## Datasets
+
+Waiting for your contribution!
+
+## Projects
+
+Waiting for your contribution!

projects/rtmpose/README.md

@@ -267,10 +267,12 @@ bash opencv.sh
 # Compile executable programs
 bash build.sh
 
-# inference for an image
+# Inference for an image
+# Please pass the folder of the model, not the model file
 ./bin/det_pose {det work-dir} {pose work-dir} {your_img.jpg} --device cpu
 
-# inference for a video
+# Inference for a video
+# Please pass the folder of the model, not the model file
 ./bin/pose_tracker {det work-dir} {pose work-dir} {your_video.mp4} --device cpu
 ```
 
@@ -296,10 +298,12 @@ bash opencv.sh
 # Compile executable programs
 bash build.sh
 
-# inference for an image
+# Inference for an image
+# Please pass the folder of the model, not the model file
 ./bin/det_pose {det work-dir} {pose work-dir} {your_img.jpg} --device cuda
 
-# inference for a video
+# Inference for a video
+# Please pass the folder of the model, not the model file
 ./bin/pose_tracker {det work-dir} {pose work-dir} {your_video.mp4} --device cuda
 ```
 
@@ -312,11 +316,21 @@ For details, see [Pipeline Inference](#-step4-pipeline-inference).
 1. Download the [pre-compiled SDK](https://github.com/open-mmlab/mmdeploy/releases).
 2. Unzip the SDK and go to the `sdk/python` folder.
 3. Install `mmdeploy_python` via `.whl` file.
+
+```shell
+pip install {file_name}.whl
+```
+
 4. Download the [sdk models](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-cpu.zip) and unzip.
 5. Inference with `pose_tracker.py`:
 
+> Note:
+
+- If you meet `ImportError: DLL load failed while importing mmdeploy_python`, please copy `thirdparty/onnxruntime/lib/onnxruntime.dll` to `site-packages/mmdeploy_python/` of your current Python env.
+
 ```shell
 # go to ./sdk/example/python
+# Please pass the folder of the model, not the model file
 python pose_tracker.py cpu {det work-dir} {pose work-dir} {your_video.mp4}
 ```
 
@@ -363,7 +377,11 @@ example\cpp\build\Release
 
 ### MMPose demo scripts
 
-MMPose provides demo scripts to conduct [inference with existing models](https://mmpose.readthedocs.io/en/1.x/user_guides/inference.html).
+MMPose provides demo scripts to conduct [inference with existing models](https://mmpose.readthedocs.io/en/latest/user_guides/inference.html).
+
+**Note:**
+
+- Inferencing with Pytorch can not reach the maximum speed of RTMPose, just for verification.
 
 ```shell
 # go to the mmpose folder

projects/rtmpose/README_CN.md

@@ -262,9 +262,11 @@ bash opencv.sh
 bash build.sh
 
 # 图片推理
+# 请传入模型目录，而不是模型文件
 ./bin/det_pose {det work-dir} {pose work-dir} {your_img.jpg} --device cpu
 
 # 视频推理
+# 请传入模型目录，而不是模型文件
 ./bin/pose_tracker {det work-dir} {pose work-dir} {your_video.mp4} --device cpu
 ```
 
@@ -290,9 +292,11 @@ bash opencv.sh
 bash build.sh
 
 # 图片推理
+# 请传入模型目录，而不是模型文件
 ./bin/det_pose {det work-dir} {pose work-dir} {your_img.jpg} --device cuda
 
 # 视频推理
+# 请传入模型目录，而不是模型文件
 ./bin/pose_tracker {det work-dir} {pose work-dir} {your_video.mp4} --device cuda
 ```
 
@@ -313,8 +317,13 @@ pip install {file_name}.whl
 4. 下载 [sdk 模型](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-cpu.zip)并解压。
 5. 使用 `pose_tracker.py` 进行推理：
 
+**提示：**
+
+- 如果遇到 `ImportError: DLL load failed while importing mmdeploy_python`，请复制 `thirdparty/onnxruntime/lib/onnxruntime.dll` 到当前环境中 python 安装目录的 `site-packages/mmdeploy_python/`。
+
 ```shell
 # 进入 ./sdk/example/python
+# 请传入模型目录，而不是模型文件
 python pose_tracker.py cpu {det work-dir} {pose work-dir} {your_video.mp4}
 ```
 
@@ -363,6 +372,10 @@ example\cpp\build\Release
 
 通过 MMPose 提供的 demo 脚本可以基于 Pytorch 快速进行[模型推理](https://mmpose.readthedocs.io/en/latest/user_guides/inference.html)和效果验证。
 
+**提示：**
+
+- 基于 Pytorch 推理并不能达到 RTMPose 模型的真实推理速度，只用于模型效果验证。
+
 ```shell
 # 前往 mmpose 目录
 cd ${PATH_TO_MMPOSE}