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 2.50, ONNX Runtime 1.27.1	Download
QNN_DLC	float	Universal	QAIRT 2.50	Download
TFLITE	float	Universal	QAIRT 2.50	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® 8 Elite Gen 5 For Galaxy Mobile	2.505 ms	0 - 102 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	2.669 ms	0 - 100 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® X2 Elite	2.61 ms	2 - 2 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® X Elite	5.636 ms	5 - 5 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	3.029 ms	0 - 126 MB	NPU
LiteHRNet	ONNX	float	Snapdragon® 8 Gen 1 Mobile	6.298 ms	1 - 122 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-8275	4.375 ms	1 - 5 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	5.335 ms	0 - 8 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® QCS8450	6.298 ms	1 - 122 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-9075	5.768 ms	1 - 4 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ IQ-X7181	5.636 ms	5 - 5 MB	NPU
LiteHRNet	ONNX	float	Qualcomm® Dragonwing™ Q-8750	2.669 ms	0 - 100 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	0.902 ms	1 - 85 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	1.072 ms	1 - 83 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® X2 Elite	1.335 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® X Elite	2.407 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	1.403 ms	0 - 99 MB	NPU
LiteHRNet	QNN_DLC	float	Snapdragon® 8 Gen 1 Mobile	2.887 ms	1 - 101 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	2.171 ms	1 - 4 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	2.093 ms	1 - 3 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® QCS8450	2.887 ms	1 - 101 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-9075	2.478 ms	3 - 5 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-X7181	2.407 ms	1 - 1 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® Dragonwing™ Q-8750	1.072 ms	1 - 83 MB	NPU
LiteHRNet	QNN_DLC	float	Qualcomm® SA8295P	3.345 ms	0 - 81 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	2.057 ms	0 - 110 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	2.229 ms	0 - 114 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	2.672 ms	0 - 146 MB	NPU
LiteHRNet	TFLITE	float	Snapdragon® 8 Gen 1 Mobile	5.209 ms	1 - 135 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	4.204 ms	1 - 12 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	4.227 ms	0 - 3 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8775P	5.278 ms	1 - 112 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8650P	5.278 ms	1 - 112 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8255P	5.278 ms	1 - 112 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® QCS8450	5.209 ms	1 - 135 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ IQ-9075	4.918 ms	1 - 12 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® Dragonwing™ Q-8750	2.229 ms	0 - 114 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA7255P	8.598 ms	1 - 111 MB	NPU
LiteHRNet	TFLITE	float	Qualcomm® SA8295P	6.016 ms	1 - 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.