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PaddleDetection
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README_cn.md
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......@@ -279,7 +279,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
- HRNet
- LiteHRNet
- [多目标跟踪](configs/mot/README.md)
- [PP-Tracking](deploy/pptracking/README.md)
- [PP-Tracking](deploy/pptracking/README_cn.md)
- [DeepSORT](configs/mot/deepsort/README_cn.md)
- [JDE](configs/mot/jde/README_cn.md)
- [FairMOT](configs/mot/fairmot/README_cn.md)
......
README_en.md
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......@@ -266,7 +266,7 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen
- [SOLOv2](configs/solov2/README.md)
- Rotation object detection
- [S2ANet](configs/dota/README_en.md)
- [Keypoint detection](configs/keypoint)
- [Keypoint detection](configs/keypoint/README_en.md)
- [PP-TinyPose](configs/keypoint/tiny_pose)
- HigherHRNet
- HRNet
......
configs/keypoint/README.md
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简体中文 | [English](README_en.md)
# KeyPoint模型系列
# 关键点检测系列模型
<div align="center">
<img src="./football_keypoint.gif" width='800'/>
</div>
## 目录
- [简介](#简介)
- [模型推荐](#模型推荐)
- [模型库](#模型库)
- [快速开始](#快速开始)
- [环境安装](#1环境安装)
- [数据准备](#2数据准备)
- [训练与测试](#3训练与测试)
- [单卡训练](#单卡训练)
- [多卡训练](#多卡训练)
- [模型评估](#模型评估)
- [模型预测](#模型预测)
- [模型部署](#模型部署)
- [Top-Down模型联合部署](#top-down模型联合部署)
- [Bottom-Up模型独立部署](#bottom-up模型独立部署)
- [与多目标跟踪联合部署](#与多目标跟踪模型fairmot联合部署预测)
## 简介
- PaddleDetection KeyPoint部分紧跟业内最新最优算法方案，包含Top-Down、BottomUp两套方案，以满足用户的不同需求。
PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包含Top-Down、Bottom-Up两套方案，Top-Down先检测主体，再检测局部关键点，优点是精度较高，缺点是速度会随着检测对象的个数增加，Bottom-Up先检测关键点再组合到对应的部位上，优点是速度快，与检测对象个数无关，缺点是精度较低。
同时，PaddleDetection提供针对移动端设备优化的自研实时关键点检测模型[PP-TinyPose](./tiny_pose/README.md)，以满足用户的不同需求。
## 模型推荐
### 移动端模型推荐
<div align="center">
<img src="./football_keypoint.gif" width='800'/>
</div>
|检测模型| 关键点模型 | 输入尺寸 | COCO数据集精度| 平均推理耗时 (FP16) | 模型权重 | Paddle-Lite部署模型（FP16)|
| :----| :------------------------ | :-------: | :------: | :------: | :---: | :---: |
| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[PP-TinyPose](./tinypose_128x96.yml) | 检测：192x192<br>关键点：128x96 | 检测mAP：29.0<br>关键点AP：58.1 | 检测耗时：2.37ms<br>关键点耗时：3.27ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) |[PP-TinyPose](./tinypose_256x192.yml)| 检测：320x320<br>关键点：256x192 | 检测mAP：38.5<br>关键点AP：68.8 | 检测耗时：6.30ms<br>关键点耗时：8.33ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams)| [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
*详细关于PP-TinyPose的使用请参考[文档]((./tiny_pose/README.md))。
#### Model Zoo
### 服务端模型推荐
|检测模型| 关键点模型 | 输入尺寸 | COCO数据集精度| 模型权重 |
| :----| :------------------------ | :-------: | :------: | :------: |
| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |[HRNet-w32](./hrnet/hrnet_w32_384x288.yml)| 检测：640x640<br>关键点：384x288 | 检测mAP：49.5<br>关键点AP：77.8 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |[HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | 检测：640x640<br>关键点：256x192 | 检测mAP：49.5<br>关键点AP：76.9 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
## 模型库
COCO数据集
| 模型 | 输入尺寸 | AP(coco val) | 模型下载 | 配置文件 |
| :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------------- |
| HigherHRNet-w32 | 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 | 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) |
| HigherHRNet-w32+SWAHR | 512 | 68.9 | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) |
| HRNet-w32 | 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | [config](./hrnet/hrnet_w32_256x192.yml) |
| HRNet-w32 | 384x288 | 77.8 | [hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | [config](./hrnet/hrnet_w32_384x288.yml) |
| HRNet-w32+DarkPose | 256x192 | 78.0 | [dark_hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) | [config](./hrnet/dark_hrnet_w32_256x192.yml) |
| HRNet-w32+DarkPose | 384x288 | 78.3 | [dark_hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) | [config](./hrnet/dark_hrnet_w32_384x288.yml) |
| WiderNaiveHRNet-18 | 256x192 | 67.6(+DARK 68.4) | [wider_naive_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/wider_naive_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml) |
| LiteHRNet-18 | 256x192 | 66.5 | [lite_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_256x192_coco.yml) |
| LiteHRNet-18 | 384x288 | 69.7 | [lite_hrnet_18_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_384x288_coco.yml) |
| LiteHRNet-30 | 256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_256x192_coco.yml) |
| LiteHRNet-30 | 384x288 | 72.5 | [lite_hrnet_30_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_384x288_coco.yml) |
| 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) |
| HigherHRNet-w32+SWAHR |Bottom-Up| 512 | 68.9 | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) |
| HRNet-w32 | Top-Down| 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | [config](./hrnet/hrnet_w32_256x192.yml) |
| HRNet-w32 |Top-Down| 384x288 | 77.8 | [hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | [config](./hrnet/hrnet_w32_384x288.yml) |
| HRNet-w32+DarkPose |Top-Down| 256x192 | 78.0 | [dark_hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) | [config](./hrnet/dark_hrnet_w32_256x192.yml) |
| HRNet-w32+DarkPose |Top-Down| 384x288 | 78.3 | [dark_hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) | [config](./hrnet/dark_hrnet_w32_384x288.yml) |
| WiderNaiveHRNet-18 | Top-Down|256x192 | 67.6(+DARK 68.4) | [wider_naive_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/wider_naive_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml) |
| LiteHRNet-18 |Top-Down| 256x192 | 66.5 | [lite_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_256x192_coco.yml) |
| LiteHRNet-18 |Top-Down| 384x288 | 69.7 | [lite_hrnet_18_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_384x288_coco.yml) |
| LiteHRNet-30 | Top-Down|256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_256x192_coco.yml) |
| LiteHRNet-30 |Top-Down| 384x288 | 72.5 | [lite_hrnet_30_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_384x288_coco.yml) |
备注： Top-Down模型测试AP结果基于GroundTruth标注框
MPII数据集
| 模型 | 输入尺寸 | PCKh(Mean) | PCKh(Mean@0.1) | 模型下载 | 配置文件 |
| :---- | -------- | :--------: | :------------: | :----------------------------------------------------------: | -------------------------------------------- |
| 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) |
| 模型 | 方案| 输入尺寸 | PCKh(Mean) | PCKh(Mean@0.1) | 模型下载 | 配置文件 |
| :---- | ---|----- | :--------: | :------------: | :----------------------------------------------------------: | -------------------------------------------- |
| 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) |
我们同时推出了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。
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。
| 模型 | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32)| 单人推理耗时（FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16)|
| :------------------------ | :-------: | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: |
| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
## 快速开始
......@@ -60,7 +99,7 @@ MPII数据集
### 3、训练与测试
​ **单卡训练：**
#### 单卡训练
```shell
#COCO DataSet
......@@ -70,7 +109,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/higherhrnet/hi
CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml
```
​ **多卡训练：**
#### 多卡训练
```shell
#COCO DataSet
......@@ -80,7 +119,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml
```
​ **模型评估：**
#### 模型评估
```shell
#COCO DataSet
......@@ -93,7 +132,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32
CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml --save_prediction_only
```
​ **模型预测：**
#### 模型预测
​ 注意：top-down模型只支持单人截图预测，如需使用多人图，请使用[联合部署推理]方式。或者使用bottom-up模型。
......@@ -101,22 +140,28 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/hig
CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=./output/higherhrnet_hrnet_w32_512/model_final.pdparams --infer_dir=../images/ --draw_threshold=0.5 --save_txt=True
```
​ **部署预测：**
#### 模型部署
##### Top-Down模型联合部署
```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
#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
#detector 检测 + keypoint top-down模型联合部署（联合推理只支持top-down方式）
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu
```
##### Bottom-Up模型独立部署
```shell
#导出模型
python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=output/higherhrnet_hrnet_w32_512/model_final.pdparams
#部署推理
#keypoint top-down/bottom-up 单独推理，该模式下top-down模型只支持单人截图预测。
python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=gpu --threshold=0.5
python deploy/python/keypoint_infer.py --model_dir=output_inference/hrnet_w32_384x288/ --image_file=./demo/hrnet_demo.jpg --device=gpu --threshold=0.5
#detector 检测 + keypoint top-down模型联合部署（联合推理只支持top-down方式）
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu
```
​ **与多目标跟踪模型FairMOT联合部署预测：**
##### 与多目标跟踪模型FairMOT联合部署预测
```shell
#导出FairMOT跟踪模型
......@@ -126,7 +171,7 @@ python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.y
python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU
```
**注意:**
跟踪模型导出教程请参考`configs/mot/README.md`。
跟踪模型导出教程请参考[文档](../mot/README.md)。
## 引用
......
configs/keypoint/README_en.md
浏览文件 @ 58f9a903
[简体中文](README.md) | English
# KeyPoint Detection Models
## Content
- [Introduction](#introduction)
- [Model Recommendation](#model-recommendation)
- [Model Zoo](#model-zoo)
- [Getting Start](#getting-start)
- [Environmental Installation](#1environmental-installation)
- [Dataset Preparation](#2dataset-preparation)
- [Training and Testing](#3training-and-testing)
- [Training on single GPU](#training-on-single-gpu)
- [Training on multiple GPU](#training-on-multiple-gpu)
- [Evaluation](#evaluation)
- [Inference](#inference)
- [Deploy Inference](#deploy-inference)
- [Deployment for Top-Down models](#deployment-for-top-down-models)
- [Deployment for Bottom-Up models](#deployment-for-bottom-up-models)
- [joint inference with Multi-Object Tracking model FairMOT](#joint-inference-with-multi-object-tracking-model-fairmot)
## Introduction
- The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and BottomUp methods, which can meet 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.
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.
At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README.md) specially for mobile devices.
<div align="center">
<img src="./football_keypoint.gif" width='800'/>
</div>
## Model Recommendation
### Mobile Terminal
|Detection Model| Keypoint Model | Input Size | Accuracy of COCO| Average Inference Time (FP16) | Model Weight | Paddle-Lite Inference Model（FP16)|
| :----| :------------------------ | :-------: | :------: | :------: | :---: | :---: |
| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[PP-TinyPose](./tinypose_128x96.yml) | Detection：192x192<br>Keypoint：128x96 | Detection mAP：29.0<br>Keypoint AP：58.1 | Detection：2.37ms<br>Keypoint：3.27ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) |[PP-TinyPose](./tinypose_256x192.yml)| Detection：320x320<br>Keypoint：256x192 | Detection mAP：38.5<br>Keypoint AP：68.8 | Detection：6.30ms<br>Keypoint：8.33ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams)| [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
*Specific documents of PP-TinyPose, please refer to [Document]((./tiny_pose/README.md))。
#### Model Zoo
### Teminal Server
|Detection Model| Keypoint Model | Input Size | Accuracy of COCO| Model Weight |
| :----| :------------------------ | :-------: | :------: | :------: |
| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |[HRNet-w32](./hrnet/hrnet_w32_384x288.yml)| Detection：640x640<br>Keypoint：384x288 | Detection mAP：49.5<br>Keypoint AP：77.8 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |[HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | Detection：640x640<br>Keypoint：256x192 | Detection mAP：49.5<br>Keypoint AP：76.9 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
## Model Zoo
COCO Dataset
| Model | Input Size | AP(coco val) | Model Download | Config File |
| :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------------- |
......@@ -44,11 +79,11 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
## Getting Start
### 1. Environmental Installation
### 1.Environmental Installation
​ Please refer to [PaddleDetection Installation Guild](../../docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
### 2. Dataset Preparation
### 2.Dataset Preparation
​ Currently, KeyPoint Detection Models support [COCO](https://cocodataset.org/#keypoints-2017) and [MPII](http://human-pose.mpi-inf.mpg.de/#overview). Please refer to [Keypoint Dataset Preparation](../../docs/tutorials/PrepareKeypointDataSet_en.md) to prepare dataset.
......@@ -56,9 +91,9 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
- Note that, when testing by detected bounding boxes in Top-Down method, We should get `bbox.json` by a detection model. You can download the detected results for COCO val2017 [(Detector having human AP of 56.4 on COCO val2017 dataset)](https://paddledet.bj.bcebos.com/data/bbox.json) directly, put it at the root path (`PaddleDetection/`), and set `use_gt_bbox: False` in config file.
### 3、Training and Testing
### 3.Training and Testing
​ **Training on single gpu:**
#### Training on single GPU
```shell
#COCO DataSet
......@@ -68,7 +103,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/higherhrnet/hi
CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml
```
​ **Training on multiple gpu:**
#### Training on multiple GPU
```shell
#COCO DataSet
......@@ -78,7 +113,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml
```
​ **Evaluation**
#### Evaluation
```shell
#COCO DataSet
......@@ -91,30 +126,38 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32
CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml --save_prediction_only
```
​ **Inference**
#### Inference
​ Note：Top-down models only support inference for a cropped image with single person. If you want to do inference on image with several people, please see "joint inference by detection and keypoint". Or you can choose a Bottom-up model.
```shell
CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=./output/higherhrnet_hrnet_w32_512/model_final.pdparams --infer_dir=../images/ --draw_threshold=0.5 --save_txt=True
```
#### Deploy Inference
##### Deployment for Top-Down models
```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
​ **Deploy Inference**
#Export Keypoint Model
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
#Deployment for detector and keypoint, which is only for Top-Down models
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu
```
##### Deployment for Bottom-Up models
```shell
#export models
#Export model
python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=output/higherhrnet_hrnet_w32_512/model_final.pdparams
#deploy inference
#keypoint inference for a single model of top-down/bottom-up method. In this mode, top-down model only support inference for a cropped image with single person.
python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=gpu --threshold=0.5
python deploy/python/keypoint_infer.py --model_dir=output_inference/hrnet_w32_384x288/ --image_file=./demo/hrnet_demo.jpg --device=gpu --threshold=0.5
#joint inference by detection and keypoint for top-down models.
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu
#Keypoint independent deployment, which is only for bottom-up models
python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=gpu --threshold=0.5
```
​ **joint inference with Multi-Object Tracking model FairMOT**
##### joint inference with Multi-Object Tracking model FairMOT
```shell
#export FairMOT model
......
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