MNASNet05: Optimized for Qualcomm Devices

MNASNet05 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

This is based on the implementation of MNASNet05 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 2.37, ONNX Runtime 1.23.0	Download
ONNX	w8a16	Universal	QAIRT 2.37, ONNX Runtime 1.23.0	Download
QNN_DLC	float	Universal	QAIRT 2.42	Download
QNN_DLC	w8a16	Universal	QAIRT 2.42	Download
TFLITE	float	Universal	QAIRT 2.42, TFLite 2.17.0	Download

For more device-specific assets and performance metrics, visit MNASNet05 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 MNASNet05 on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

• Model checkpoint: Imagenet
• Input resolution: 224x224
• Number of parameters: 2.21M
• Model size (float): 8.45 MB
• Model size (w8a16): 2.79 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
MNASNet05	ONNX	float	Snapdragon® X Elite	0.617 ms	5 - 5 MB	NPU
MNASNet05	ONNX	float	Snapdragon® 8 Gen 3 Mobile	0.493 ms	0 - 116 MB	NPU
MNASNet05	ONNX	float	Qualcomm® QCS8550 (Proxy)	0.691 ms	0 - 12 MB	NPU
MNASNet05	ONNX	float	Qualcomm® QCS9075	0.97 ms	1 - 3 MB	NPU
MNASNet05	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	0.381 ms	0 - 99 MB	NPU
MNASNet05	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	0.317 ms	1 - 99 MB	NPU
MNASNet05	ONNX	w8a16	Snapdragon® X Elite	0.645 ms	2 - 2 MB	NPU
MNASNet05	ONNX	w8a16	Snapdragon® 8 Gen 3 Mobile	0.521 ms	0 - 111 MB	NPU
MNASNet05	ONNX	w8a16	Qualcomm® QCS6490	29.306 ms	8 - 11 MB	CPU
MNASNet05	ONNX	w8a16	Qualcomm® QCS8550 (Proxy)	0.705 ms	0 - 44 MB	NPU
MNASNet05	ONNX	w8a16	Qualcomm® QCS9075	0.899 ms	0 - 3 MB	NPU
MNASNet05	ONNX	w8a16	Qualcomm® QCM6690	18.804 ms	9 - 16 MB	CPU
MNASNet05	ONNX	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.368 ms	0 - 100 MB	NPU
MNASNet05	ONNX	w8a16	Snapdragon® 7 Gen 4 Mobile	13.001 ms	9 - 16 MB	CPU
MNASNet05	ONNX	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.329 ms	0 - 100 MB	NPU
MNASNet05	QNN_DLC	float	Snapdragon® X Elite	0.916 ms	1 - 1 MB	NPU
MNASNet05	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	0.516 ms	0 - 46 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	2.318 ms	1 - 30 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	0.775 ms	1 - 2 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® SA8775P	1.091 ms	1 - 31 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® QCS9075	0.974 ms	1 - 3 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	1.579 ms	0 - 48 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® SA7255P	2.318 ms	1 - 30 MB	NPU
MNASNet05	QNN_DLC	float	Qualcomm® SA8295P	1.416 ms	0 - 28 MB	NPU
MNASNet05	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	0.379 ms	0 - 33 MB	NPU
MNASNet05	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	0.291 ms	1 - 33 MB	NPU
MNASNet05	QNN_DLC	w8a16	Snapdragon® X Elite	0.924 ms	0 - 0 MB	NPU
MNASNet05	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	0.53 ms	0 - 37 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCS6490	2.225 ms	2 - 4 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCS8275 (Proxy)	1.665 ms	0 - 26 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCS8550 (Proxy)	0.785 ms	0 - 2 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® SA8775P	4.096 ms	0 - 27 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCS9075	0.925 ms	0 - 2 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCM6690	3.052 ms	0 - 138 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® QCS8450 (Proxy)	0.958 ms	0 - 39 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® SA7255P	1.665 ms	0 - 26 MB	NPU
MNASNet05	QNN_DLC	w8a16	Qualcomm® SA8295P	1.235 ms	0 - 23 MB	NPU
MNASNet05	QNN_DLC	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.361 ms	0 - 25 MB	NPU
MNASNet05	QNN_DLC	w8a16	Snapdragon® 7 Gen 4 Mobile	0.785 ms	0 - 24 MB	NPU
MNASNet05	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.29 ms	0 - 29 MB	NPU
MNASNet05	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	0.519 ms	0 - 47 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® QCS8275 (Proxy)	2.331 ms	0 - 30 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® QCS8550 (Proxy)	0.776 ms	0 - 1 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® SA8775P	1.107 ms	0 - 33 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® QCS9075	0.978 ms	0 - 8 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® QCS8450 (Proxy)	1.581 ms	0 - 49 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® SA7255P	2.331 ms	0 - 30 MB	NPU
MNASNet05	TFLITE	float	Qualcomm® SA8295P	1.432 ms	0 - 29 MB	NPU
MNASNet05	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	0.384 ms	0 - 35 MB	NPU
MNASNet05	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	0.29 ms	0 - 34 MB	NPU

License

• The license for the original implementation of MNASNet05 can be found here.

References

• MnasNet: Platform-Aware Neural Architecture Search for Mobile
• 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.