LiteHRNet: Optimized for Qualcomm Devices

LiteHRNet 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 LiteHRNet 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.

Runtime	Precision	Chipset	SDK Versions	Download
ONNX	float	Universal	QAIRT 2.45, ONNX Runtime 1.27.1	Download
QNN_DLC	float	Universal	QAIRT 2.45	Download
TFLITE	float	Universal	QAIRT 2.45	Download

For more device-specific assets and performance metrics, visit LiteHRNet 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 LiteHRNet on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.pose_estimation

Model Stats:

• Input resolution: 256x192
• Model size (float): 4.49 MB
• Number of parameters: 1.11M

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
LiteHRNet	ONNX	float	Snapdragon® X2 Elite	2.884 ms	2 - 2 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® X Elite	5.743 ms	6 - 6 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	3.115 ms	0 - 124 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Gen 1 Mobile	6.302 ms	1 - 121 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-8275	4.469 ms	1 - 5 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	5.42 ms	1 - 22 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® QCS8450	6.302 ms	1 - 121 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-9075	5.767 ms	1 - 4 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-X7181	5.743 ms	6 - 6 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ Q-8750	2.875 ms	0 - 100 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Elite Mobile	2.875 ms	0 - 100 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	2.782 ms	0 - 100 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® X2 Elite	1.351 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® X Elite	2.417 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	1.404 ms	0 - 104 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Gen 1 Mobile	2.955 ms	0 - 102 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	2.197 ms	1 - 4 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	5.061 ms	1 - 78 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	2.12 ms	1 - 94 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA8775P	2.707 ms	1 - 78 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA8650P	2.707 ms	1 - 78 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA8255P	2.707 ms	1 - 78 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® QCS8450	2.955 ms	0 - 102 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-9075	2.522 ms	1 - 3 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-X7181	2.417 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ Q-8750	1.077 ms	0 - 81 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA7255P	5.061 ms	1 - 78 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA8295P	3.494 ms	0 - 82 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Elite Mobile	1.077 ms	0 - 81 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	0.916 ms	1 - 84 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	2.688 ms	0 - 147 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Gen 1 Mobile	5.361 ms	1 - 138 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	4.216 ms	1 - 12 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	8.85 ms	1 - 116 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	4.227 ms	0 - 2 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8775P	5.364 ms	1 - 116 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8650P	5.364 ms	1 - 116 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8255P	5.364 ms	1 - 116 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® QCS8450	5.361 ms	1 - 138 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ IQ-9075	4.911 ms	1 - 11 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ Q-8750	2.245 ms	0 - 117 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA7255P	8.85 ms	1 - 116 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8295P	6.333 ms	1 - 113 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Elite Mobile	2.245 ms	0 - 117 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	2.043 ms	0 - 111 MB	NPU

License

• The license for the original implementation of LiteHRNet can be found here.

References

• Lite-HRNet: A Lightweight High-Resolution Network
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.