EfficientNet-B4: Optimized for Qualcomm Devices

EfficientNetB4 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 EfficientNet-B4 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 2.42, ONNX Runtime 1.24.1	Download
ONNX	w8a16	Universal	QAIRT 2.42, ONNX Runtime 1.24.1	Download
QNN_DLC	float	Universal	QAIRT 2.43	Download
QNN_DLC	w8a16	Universal	QAIRT 2.43	Download
TFLITE	float	Universal	QAIRT 2.43, TFLite 2.17.0	Download

For more device-specific assets and performance metrics, visit EfficientNet-B4 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 EfficientNet-B4 on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

• Model checkpoint: Imagenet
• Input resolution: 380x380
• Number of parameters: 19.3M
• Model size (float): 73.6 MB
• Model size (w8a16): 24.0 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
EfficientNet-B4	ONNX	float	Snapdragon® X2 Elite	1.631 ms	45 - 45 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® X Elite	3.362 ms	45 - 45 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® 8 Gen 3 Mobile	2.262 ms	0 - 130 MB	NPU
EfficientNet-B4	ONNX	float	Qualcomm® QCS8550 (Proxy)	3.071 ms	0 - 50 MB	NPU
EfficientNet-B4	ONNX	float	Qualcomm® QCS9075	4.011 ms	0 - 4 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	1.77 ms	0 - 79 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	1.467 ms	0 - 76 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® X2 Elite	1.965 ms	1 - 1 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® X Elite	3.65 ms	1 - 1 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	2.418 ms	0 - 125 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	11.996 ms	1 - 69 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	3.358 ms	1 - 2 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS9075	4.199 ms	1 - 3 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	7.799 ms	0 - 143 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	1.869 ms	1 - 73 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	1.508 ms	1 - 73 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® X2 Elite	1.656 ms	0 - 0 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® X Elite	3.837 ms	0 - 0 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	2.308 ms	0 - 151 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS6490	8.706 ms	2 - 4 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8275 (Proxy)	6.666 ms	0 - 99 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8550 (Proxy)	3.464 ms	0 - 2 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS9075	3.805 ms	0 - 2 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCM6690	15.762 ms	0 - 231 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8450 (Proxy)	4.118 ms	0 - 153 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	1.61 ms	0 - 104 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 7 Gen 4 Mobile	3.597 ms	0 - 109 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	1.321 ms	0 - 102 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	2.412 ms	0 - 168 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8275 (Proxy)	11.996 ms	0 - 105 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8550 (Proxy)	3.349 ms	0 - 2 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS9075	4.202 ms	0 - 48 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8450 (Proxy)	7.835 ms	0 - 187 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	1.859 ms	0 - 109 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	1.511 ms	0 - 107 MB	NPU

License

• The license for the original implementation of EfficientNet-B4 can be found here.

References

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• 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.