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[Docs] Update Docs in RTMPose #2057

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Mar 14, 2023
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[Docs] Update Docs in RTMPose #2057

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Update projects/rtmpose/README.md
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14 changes: 8 additions & 6 deletions projects/rtmpose/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -52,10 +52,12 @@ Coming soon

- 🚀 **High efficiency and high accuracy**

- t | 68.5 AP on COCO with CPU: 300+ FPS / GPU: 940+ FPS
- s | 72.2 AP on COCO with CPU: 200+ FPS / GPU: 710+ FPS
- m | 75.8 AP on COCO with CPU: 90+ FPS / GPU: 430+ FPS
- l | 76.5 AP on COCO with CPU: 50+ FPS / GPU: 280+ FPS
| Model | AP(COCO) | CPU-FPS | GPU-FPS |
| :---: | :------: | :-----: | :-----: |
| t | 68.5 | 300+ | 940+ |
| s | 72.2 | 200+ | 710+ |
| m | 75.8 | 90+ | 430+ |
| l | 76.5 | 50+ | 280+ |

- 🛠️ **Easy to deploy**

Expand Down Expand Up @@ -217,7 +219,7 @@ We provide two appoaches to try RTMPose:

### Pre-compiled MMDeploy SDK (Recommended)

MMDeploy provides pre-compiled SDK to conduct inference with RTMPose projects.
MMDeploy provides a precompiled SDK for Pipeline reasoning on RTMPose projects, where the model used for reasoning is the SDK version. For the tutorial of exporting the SDK version model, see [SDK Reasoning](#%EF%B8%8F-step3-inference-with-sdk), and for detailed parameter settings of inference, see [Pipeline Reasoning](#-step4-pipeline-inference).

Env Requirements:

Expand Down Expand Up @@ -423,7 +425,7 @@ python tools/deploy.py \

The converted model file is `{work-dir}/end2end.engine` by defaults.

If the script runs successfully, you will see the following files:
🎊 If the script runs successfully, you will see the following files:

![convert_models](https://user-images.githubusercontent.com/13503330/217726963-7815dd01-561a-4605-b0c6-07b6fe1956c3.png)

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14 changes: 8 additions & 6 deletions projects/rtmpose/README_CN.md
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Original file line number Diff line number Diff line change
Expand Up @@ -48,10 +48,12 @@ Coming soon

- 🚀 **高精度，低延迟**

- t | COCO 68.5 AP | CPU: 300+ FPS / GPU: 940+ FPS
- s | COCO 72.2 AP | CPU: 200+ FPS / GPU: 710+ FPS
- m | COCO 75.8 AP | CPU: 90+ FPS / GPU: 430+ FPS
- l | COCO 76.5 AP | CPU: 50+ FPS / GPU: 280+ FPS
| Model | AP(COCO) | CPU-FPS | GPU-FPS |
| :---: | :------: | :-----: | :-----: |
| t | 68.5 | 300+ | 940+ |
| s | 72.2 | 200+ | 710+ |
| m | 75.8 | 90+ | 430+ |
| l | 76.5 | 50+ | 280+ |

- 🛠️ **易部署**

Expand Down Expand Up @@ -212,7 +214,7 @@ RTMPose 是一个长期优化迭代的项目，致力于业务场景下的高性

### MMDeploy SDK 预编译包 （推荐）

MMDeploy 提供了预编译的 SDK，用于对 RTMPose 项目进行推理。
MMDeploy 提供了预编译的 SDK，用于对 RTMPose 项目进行 Pipeline 推理，其中推理所用的模型为 SDK 版本。导出 SDK 版模型的教程见 [SDK 推理](#%EF%B8%8F-sdk-推理)，推理的详细参数设置见 [Pipeline 推理](#-pipeline-推理)。

说明：

Expand Down Expand Up @@ -423,7 +425,7 @@ python tools/deploy.py \

默认导出模型文件为 `{work-dir}/end2end.engine`

如果模型顺利导出，你将会看到样例图片上的检测结果：
🎊 如果模型顺利导出，你将会看到样例图片上的检测结果：

![convert_models](https://user-images.githubusercontent.com/13503330/217726963-7815dd01-561a-4605-b0c6-07b6fe1956c3.png)

Expand Down