FastDeploy/examples/vision/keypointdetection/tiny_pose

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PP-TinyPose Model Deployment

Model Description

• PaddleDetection release/2.5

Now FastDeploy supports the deployment of the following models

• PP-TinyPose models

Prepare PP-TinyPose Deployment Model

Export the PP-TinyPose model. Please refer to Model Export

Attention: The exported PP-TinyPose 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-TinyPose exported models are provided below. Developers can download and use them directly.

Model	Parameter File Size	Input Shape	AP(Service Data set)	AP(COCO Val)	FLOPS	Single/Multi-person Inference Time (FP32)	Single/Multi-person Inference Time（FP16)
PP-TinyPose-128x96	5.3MB	128x96	84.3%	58.4%	81.56 M	4.57ms	3.27ms
PP-TinyPose-256x192	5.3M	256x96	91.0%	68.3%	326.24M	14.07ms	8.33ms

Note

• The keypoint detection model uses COCO train2017 and AI Challenger trainset as the training sets and COCO person keypoints val2017 as the test set.
• The detection frame, through which we get the accuracy of the keypoint detection model, is obtained from the ground truth annotation.
• The speed test environment is Qualcomm Snapdragon 865 with 4-thread inference under arm8.

For more information: refer to PP-TinyPose official document

Detailed Deployment Tutorials

• Python Deployment
• C++ Deployment