det_keypoint_unite

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PP-PicoDet + PP-TinyPose Co-deployment (Pipeline)

Model Description

• PaddleDetection release/2.5

Now FastDeploy supports the deployment of the following models

• PP-PicoDet + PP-TinyPose Models

Prepare PP-TinyPose Deployment Model

Export the PP-TinyPose and PP-PicoDet models. Please refer to Model Export

Attention: The exported inference model contains three files, including model.pdmodel、model.pdiparams and infer_cfg.yml. FastDeploy will get the pre-processing information for inference from yaml files.

Download Pre-trained Model

For developers' testing, part of the PP-PicoDet + PP-TinyPose（Pipeline）exported models are provided below. Developers can download and use them directly.

Application Scenario	Model	Parameter File Size	AP(Service Data set)	AP(COCO Val Single/Multi-person)	Single/Multi-person Inference Time (FP32)	Single/Multi-person Inference Time（FP16)
Single-person Model Configuration	PicoDet-S-Lcnet-Pedestrian-192x192 + PP-TinyPose-128x96	4.6MB + 5.3MB	86.2%	52.8%	12.90ms	9.61ms
Multi-person Model Configuration	PicoDet-S-Lcnet-Pedestrian-320x320 + PP-TinyPose-256x192	4.6M + 5.3MB	85.7%	49.9%	47.63ms	34.62ms

Note

• The accuracy of the keypoint detection model is based on the detection frame obtained by the pedestrian detection model.
• The flip operation is removed from the accuracy test with the detection confidence threshold of 0.5.
• The speed test environment is qualcomm snapdragon 865 with 4-thread inference under arm8.
• The Pipeline speed covers the preprocessing, inference, and post-processing of the model.
• In the accuracy test, images with more than 6 people (excluding 6 people) were removed from the multi-person data for fair comparison.

For more information: refer to PP-TinyPose official document

Detailed Deployment Tutorials

• Python Deployment
• C++ Deployment