未验证 提交 bb4fbe84 编写于 作者: J JYChen 提交者: GitHub

[Cherry-pick ] add pdparams links (#5866)

* fix kpt doc error

* add pdparams links
上级 530fb7c4
...@@ -52,7 +52,6 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案,包 ...@@ -52,7 +52,6 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案,包
## 模型库 ## 模型库
## 模型库
COCO数据集 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 |
| :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------| ------- | | :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------| ------- |
...@@ -77,12 +76,12 @@ MPII数据集 ...@@ -77,12 +76,12 @@ 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) | | 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。 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 ## 快速开始
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...@@ -82,6 +82,12 @@ MPII Dataset ...@@ -82,6 +82,12 @@ 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) | | 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. 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 ## Getting Start
...@@ -145,7 +151,6 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi ...@@ -145,7 +151,6 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi
##### Deployment for Top-Down models ##### Deployment for Top-Down models
```shell ```shell
#Export Detection Model #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
......
...@@ -40,14 +40,14 @@ pip install -r requirements.txt ...@@ -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 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 | | 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.zip) | | 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.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) | | 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) | 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) | | 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) | | 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 下载模型后,解压至`./output_inference`文件夹
......
...@@ -41,13 +41,14 @@ pip install -r requirements.txt ...@@ -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. 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 | | 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.zip) | | 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) |
| 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) | | 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) |
| 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) | 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) |
| Behavior Recognition | Video Input Bheavior Recognition | Precision 96.43 | 2.7ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.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)
| 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) | | 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`. Then, unzip the downloaded model to the folder `./output_inference`.
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[English](action_en.md) | 简体中文
# PP-Human行为识别模块 # 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。 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 @@ ...@@ -10,11 +12,11 @@
## 模型库 ## 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。 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 | | 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 <br> MOTA: 72.0 | 检测: 28ms <br> 跟踪:33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | 行人检测/跟踪 | PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | 检测: 28ms <br> 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.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) | | 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: 注:
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English | [简体中文](action.md)
# Action Recognition Module of PP-Human # 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. 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. </center> ...@@ -12,11 +14,11 @@ used for academic research here. </center>
There are multiple available pretrained models including pedestrian detection/tracking, keypoint detection, and fall detection models. Users can download and use them directly. 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 | | Task | Algorithm | Precision | Inference Speed(ms) | Model Weights |Model Inference and Deployment |
|:----------------------------- |:---------:|:-------------------------:|:-----------------------------------:|:-----------------------------------------------------------------------------------------:| |:----------------------------- |:---------:|:-------------------------:|:-----------------------------------:| :-----------------: |:-----------------------------------------------------------------------------------------:|
| Pedestrian Detection/Tracking | PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | Detection: 28ms <br>Tracking:33.1ms | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | Pedestrian Detection/Tracking | PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | Detection: 28ms <br>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.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) | | Action Recognition | ST-GCN | Precision Rate: 96.43 | Single Person 2.7ms | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
Note: Note:
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[English](mot_en.md) | 简体中文
# PP-Human检测跟踪模块 # 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。 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。
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English | [简体中文](mot.md)
# Detection and Tracking Module of PP-Human # 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. 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.
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[English](mtmct_en.md) | 简体中文
# PP-Human跨镜头跟踪模块 # 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。 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。
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English | [简体中文](mtmct.md)
# Multi-Target Multi-Camera Tracking Module of PP-Human # 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. 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.
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