CenterNet-Pose: Optimized for Qualcomm Devices

CenterNet-Pose is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.

This is based on the implementation of CenterNet-Pose found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

For more device-specific assets and performance metrics, visit CenterNet-Pose on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• Custom weights (e.g., fine-tuned checkpoints)
• Custom input shapes
• Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for CenterNet-Pose on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.pose_estimation

Model Stats:

• Input resolution: 1 x 3 x 512 x 512
• Model checkpoint: multi_pose_dla_3x.pth
• Model size: 57.8 MB
• Number of parameters: 20.6M

Performance Summary

License

• The license for the original implementation of CenterNet-Pose can be found here.

References

• Objects as Points
• Source Model Implementation

Community

• For questions or feedback, chat with our engineers in Qualcomm® AI Hub Workbench.