diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md
index 1dc5e9d028d5165126fe891841382be8983cd5bf..b00d9dda2f20a6758735533e447bdc81baa17046 100644
--- a/configs/keypoint/README.md
+++ b/configs/keypoint/README.md
@@ -66,7 +66,7 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包
## 模型库
COCO数据集
-| 模型 | 方案 |输入尺寸 | AP(coco val) | 模型下载 | 配置文件 |
+| 模型 | 方案 |输入尺寸 | AP(coco val) | 模型下载 | 配置文件 |
| :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------| ------- |
| HigherHRNet-w32 |Bottom-Up| 512 | 67.1 | [higherhrnet_hrnet_w32_512.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512.yml) |
| HigherHRNet-w32 | Bottom-Up| 640 | 68.3 | [higherhrnet_hrnet_w32_640.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_640.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_640.yml) |
@@ -106,8 +106,16 @@ MPII数据集
| :---- | ---|----- | :--------: | :------------: | :----------------------------------------------------------: | -------------------------------------------- |
| HRNet-w32 | Top-Down|256x256 | 90.6 | 38.5 | [hrnet_w32_256x256_mpii.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x256_mpii.pdparams) | [config](./hrnet/hrnet_w32_256x256_mpii.yml) |
+场景模型
+| 模型 | 方案 | 输入尺寸 | 精度 | 预测速度 |模型权重 | 部署模型 | 说明|
+| :---- | ---|----- | :--------: | :--------: | :------------: |:------------: |:-------------------: |
+| HRNet-w32 + DarkPose | Top-Down|256x192 | AP: 87.1 (业务数据集)| 单人2.9ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | 针对摔倒场景特别优化，该模型应用于[PP-Human](../../deploy/pphuman/README.md) |
+
+
我们同时推出了基于LiteHRNet（Top-Down）针对移动端设备优化的实时关键点检测模型[PP-TinyPose](./tiny_pose/README.md), 欢迎体验。
+
+
## 快速开始
### 1、环境安装
@@ -172,7 +180,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi
```shell
#导出检测模型
-python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
+python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
#导出关键点模型
python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams
diff --git a/configs/keypoint/README_en.md b/configs/keypoint/README_en.md
index 2c1ba042d385cbd120cca3d01650b4ecc08be748..68b738bd1e16fb097c42de310356ff9e85426186 100644
--- a/configs/keypoint/README_en.md
+++ b/configs/keypoint/README_en.md
@@ -27,9 +27,9 @@
## Introduction
-The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and Bottom-Up methods, which can satisfy the different needs of users.
+The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and Bottom-Up methods, which can satisfy the different needs of users.
-Top-Down detects the object first and then detect the specific keypoint. The accuracy of Top-Down models will be higher, but the time required will increase by the number of objects.
+Top-Down detects the object first and then detect the specific keypoint. The accuracy of Top-Down models will be higher, but the time required will increase by the number of objects.
Differently, Bottom-Up detects the point first and then group or connect those points to form several instances of human pose. The speed of Bottom-Up is fixed and will not increase by the number of objects, but the accuracy will be lower.
@@ -89,6 +89,13 @@ MPII Dataset
| :---- | -------- | :--------: | :------------: | :----------------------------------------------------------: | -------------------------------------------- |
| HRNet-w32 | 256x256 | 90.6 | 38.5 | [hrnet_w32_256x256_mpii.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x256_mpii.pdparams) | [config](./hrnet/hrnet_w32_256x256_mpii.yml) |
+
+Model for Scenes
+| Model | Strategy | Input Size | Precision | Inference Speed |Model Weights | Model Inference and Deployment | description|
+| :---- | ---|----- | :--------: | :-------: |:------------: |:------------: |:-------------------: |
+| HRNet-w32 + DarkPose | Top-Down|256x192 | AP: 87.1 (on internal dataset)| 2.9ms per person |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | Especially optimized for fall scenarios, the model is applied to [PP-Human](../../deploy/pphuman/README_en.md) |
+
+
We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint detection model optimized for mobile devices. Welcome to experience.
## Getting Start
@@ -155,7 +162,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi
```shell
#Export Detection Model
-python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
+python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
#Export Keypoint Model
diff --git a/deploy/pphuman/README.md b/deploy/pphuman/README.md
index f5aa67b1b3dc04508ea856fc5ad6fbdbf8aab2b5..0496f3f467906aba255940cc4fc3f07ba1911d57 100644
--- a/deploy/pphuman/README.md
+++ b/deploy/pphuman/README.md
@@ -40,14 +40,14 @@ pip install -r requirements.txt
PP-Human提供了目标检测、属性识别、行为识别、ReID预训练模型，以实现不同使用场景，用户可以直接下载使用
-| 任务 | 适用场景 | 精度 | 预测速度（ms） | 预测部署模型 |
-| :---------: |:---------: |:--------------- | :-------: | :------: |
-| 目标检测 | 图片输入 | mAP: 56.3 | 28.0ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| 目标跟踪 | 视频输入 | MOTA: 72.0 | 33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| 属性识别 | 图片/视频输入 属性识别 | mA: 94.86 | 单人2ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
-| 关键点检测 | 视频输入 行为识别 | AP: 87.1 | 单人2.9ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
-| 行为识别 | 视频输入 行为识别 | 准确率: 96.43 | 单人2.7ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
-| ReID | 视频输入 跨镜跟踪 | mAP: 98.8 | 单人1.5ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) |
+| 任务 | 适用场景 | 精度 | 预测速度（ms） | 模型权重 | 预测部署模型 |
+| :---------: |:---------: |:--------------- | :-------: | :------: | :------: |
+| 目标检测 | 图片输入 | mAP: 56.3 | 28.0ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 目标跟踪 | 视频输入 | MOTA: 72.0 | 33.1ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 属性识别 | 图片/视频输入 属性识别 | mA: 94.86 | 单人2ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
+| 关键点检测 | 视频输入 行为识别 | AP: 87.1 | 单人2.9ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
+| 行为识别 | 视频输入 行为识别 | 准确率: 96.43 | 单人2.7ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
+| ReID | 视频输入 跨镜跟踪 | mAP: 98.8 | 单人1.5ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) |
下载模型后，解压至`./output_inference`文件夹
diff --git a/deploy/pphuman/README_en.md b/deploy/pphuman/README_en.md
index fc57e86c3cba0ef2b292d97e0791f80da86bccf4..735f817d43cf62363cc2fa05da3e13bf04c9283a 100644
--- a/deploy/pphuman/README_en.md
+++ b/deploy/pphuman/README_en.md
@@ -41,13 +41,14 @@ pip install -r requirements.txt
To make users have access to models of different scenarios, PP-Human provides pre-trained models of object detection, attribute recognition， behavior recognition, and ReID.
-| Task | Scenario | Precision | Inference Speed（FPS） | Model Inference and Deployment |
-| :---------: |:---------: |:--------------- | :-------: | :------: |
-| Object Detection | Image/Video Input | mAP: 56.3 | 28.0ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| Attribute Recognition | Image/Video Input Attribute Recognition | MOTA: 72.0 | 33.1ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
-| Keypoint Detection | Video Input Action Recognition | mA: 94.86 | 2ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
-| Behavior Recognition | Video Input Bheavior Recognition | Precision 96.43 | 2.7ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
-| ReID | Multi-Target Multi-Camera Tracking | mAP: 98.8 | 1.5ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) |
+| Task | Scenario | Precision | Inference Speed（FPS） | Model Weights |Model Inference and Deployment |
+| :---------: |:---------: |:--------------- | :-------: | :------: | :------: |
+| Object Detection | Image/Video Input | mAP: 56.3 | 28.0ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Object Tracking | Image/Video Input | MOTA: 72.0 | 33.1ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Attribute Recognition | Image/Video Input Attribute Recognition | mA: 94.86 | 2ms per person | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
+| Keypoint Detection | Video Input Action Recognition | AP: 87.1 | 2.9ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
+| Action Recognition | Video Input Action Recognition | Precision 96.43 | 2.7ms per person | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
+| ReID | Multi-Target Multi-Camera Tracking | mAP: 98.8 | 1.5ms per person | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) |
Then, unzip the downloaded model to the folder `./output_inference`.
diff --git a/deploy/pphuman/docs/action.md b/deploy/pphuman/docs/action.md
index f23654299058137dc852e6efb8b71e5b18493ab4..96cb35e4f6804caceb4574ac6bb7c905aba008ab 100644
--- a/deploy/pphuman/docs/action.md
+++ b/deploy/pphuman/docs/action.md
@@ -1,3 +1,5 @@
+[English](action_en.md) | 简体中文
+
# PP-Human行为识别模块
行为识别在智慧社区，安防监控等方向具有广泛应用，PP-Human中集成了基于骨骼点的行为识别模块。
@@ -10,11 +12,11 @@
## 模型库
在这里，我们提供了检测/跟踪、关键点识别以及识别摔倒动作的预训练模型，用户可以直接下载使用。
-| 任务 | 算法 | 精度 | 预测速度(ms) | 下载链接 |
-|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | 检测: 28ms
跟踪：33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| 关键点识别 | HRNet | AP: 87.1 | 单人 2.9ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)|
-| 行为识别 | ST-GCN | 准确率: 96.43 | 单人 2.7ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
+| 任务 | 算法 | 精度 | 预测速度(ms) | 模型权重 | 预测部署模型 |
+|:---------------------|:---------:|:------:|:------:| :------: |:---------------------------------------------------------------------------------: |
+| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | 检测: 28ms
跟踪：33.1ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 关键点识别 | HRNet | AP: 87.1 | 单人 2.9ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)|
+| 行为识别 | ST-GCN | 准确率: 96.43 | 单人 2.7ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
注：
diff --git a/deploy/pphuman/docs/action_en.md b/deploy/pphuman/docs/action_en.md
index ba33efc4c0af419649e21f58205a3ff635b7efb0..f2c8e8fc80453a764e9139ae1e9ded73e344d3f3 100644
--- a/deploy/pphuman/docs/action_en.md
+++ b/deploy/pphuman/docs/action_en.md
@@ -1,3 +1,5 @@
+English | [简体中文](action.md)
+
# Action Recognition Module of PP-Human
Action Recognition is widely used in the intelligent community/smart city, and security monitoring. PP-Human provides the module of skeleton-based action recognition.
@@ -12,11 +14,11 @@ used for academic research here.
There are multiple available pretrained models including pedestrian detection/tracking, keypoint detection, and fall detection models. Users can download and use them directly.
-| Task | Algorithm | Precision | Inference Speed(ms) | Download Link |
-|:----------------------------- |:---------:|:-------------------------:|:-----------------------------------:|:-----------------------------------------------------------------------------------------:|
-| Pedestrian Detection/Tracking | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | Detection: 28ms
Tracking：33.1ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| Keypoint Detection | HRNet | AP: 87.1 | Single Person 2.9ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) |
-| Action Recognition | ST-GCN | Precision Rate: 96.43 | Single Person 2.7ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
+| Task | Algorithm | Precision | Inference Speed(ms) | Model Weights |Model Inference and Deployment |
+|:----------------------------- |:---------:|:-------------------------:|:-----------------------------------:| :-----------------: |:-----------------------------------------------------------------------------------------:|
+| Pedestrian Detection/Tracking | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | Detection: 28ms
Tracking：33.1ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Keypoint Detection | HRNet | AP: 87.1 | Single Person 2.9ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) |
+| Action Recognition | ST-GCN | Precision Rate: 96.43 | Single Person 2.7ms | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
Note:
diff --git a/deploy/pphuman/docs/mot.md b/deploy/pphuman/docs/mot.md
index d9487a3941358d14d410217f1e97bec490cbbfde..0552c4df2f807d6b3003258be9c7638bed63bbca 100644
--- a/deploy/pphuman/docs/mot.md
+++ b/deploy/pphuman/docs/mot.md
@@ -1,3 +1,5 @@
+[English](mot_en.md) | 简体中文
+
# PP-Human检测跟踪模块
行人检测与跟踪在智慧社区，工业巡检，交通监控等方向都具有广泛应用，PP-Human中集成了检测跟踪模块，是关键点检测、属性行为识别等任务的基础。我们提供了预训练模型，用户可以直接下载使用。
diff --git a/deploy/pphuman/docs/mot_en.md b/deploy/pphuman/docs/mot_en.md
index 2c657e8eceae4abd60aa02adbc2d06425461064a..61a970898dc8ceab27c5d1b38782d44efbe961a2 100644
--- a/deploy/pphuman/docs/mot_en.md
+++ b/deploy/pphuman/docs/mot_en.md
@@ -1,3 +1,5 @@
+English | [简体中文](mot.md)
+
# Detection and Tracking Module of PP-Human
Pedestrian detection and tracking is widely used in the intelligent community, industrial inspection, transportation monitoring and so on. PP-Human has the detection and tracking module, which is fundamental to keypoint detection, attribute action recognition, etc. Users enjoy easy access to pretrained models here.
diff --git a/deploy/pphuman/docs/mtmct.md b/deploy/pphuman/docs/mtmct.md
index 1896d1c1f6124a74007323e8f20f7872283d449a..5e2eacef80c03bcd3d4399ad7b377e18f244835e 100644
--- a/deploy/pphuman/docs/mtmct.md
+++ b/deploy/pphuman/docs/mtmct.md
@@ -1,3 +1,5 @@
+[English](mtmct_en.md) | 简体中文
+
# PP-Human跨镜头跟踪模块
跨镜头跟踪任务，是在单镜头跟踪的基础上，实现不同摄像头中人员的身份匹配关联。在安放、智慧零售等方向有较多的应用。
diff --git a/deploy/pphuman/docs/mtmct_en.md b/deploy/pphuman/docs/mtmct_en.md
index 117e2d78ea84adc2391111764dd3252f1ebcf8ba..935aa1442ed53feb7faac9ebb0f0ca0ecc91fa03 100644
--- a/deploy/pphuman/docs/mtmct_en.md
+++ b/deploy/pphuman/docs/mtmct_en.md
@@ -1,3 +1,5 @@
+English | [简体中文](mtmct.md)
+
# Multi-Target Multi-Camera Tracking Module of PP-Human
Multi-target multi-camera tracking, or MTMCT, matches the identity of a person in different cameras based on the single-camera tracking. MTMCT is usually applied to the security system and the smart retailing.