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 1.24.3	Download
QNN_DLC	float	Universal	QAIRT 2.43	Download
QNN_DLC	w8a16	Universal	QAIRT 2.43	Download
TFLITE	float	Universal	QAIRT 2.43, TFLite 2.19.1	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® 8 Elite Gen 5 Mobile	1.469 ms	0 - 77 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® X2 Elite	1.632 ms	45 - 45 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® X Elite	3.343 ms	45 - 45 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® 8 Gen 3 Mobile	2.259 ms	0 - 129 MB	NPU
EfficientNet-B4	ONNX	float	Qualcomm® QCS8550 (Proxy)	3.062 ms	0 - 50 MB	NPU
EfficientNet-B4	ONNX	float	Qualcomm® QCS9075	4.021 ms	1 - 4 MB	NPU
EfficientNet-B4	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	1.752 ms	0 - 73 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	1.515 ms	0 - 73 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® X2 Elite	1.953 ms	1 - 1 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® X Elite	3.651 ms	1 - 1 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	2.407 ms	0 - 126 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	12.007 ms	1 - 69 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	3.351 ms	1 - 3 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS9075	4.202 ms	1 - 3 MB	NPU
EfficientNet-B4	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	7.871 ms	0 - 143 MB	NPU
EfficientNet-B4	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	1.86 ms	1 - 73 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	1.322 ms	0 - 102 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® X2 Elite	1.69 ms	0 - 0 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® X Elite	3.805 ms	0 - 0 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	2.318 ms	0 - 151 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS6490	8.767 ms	0 - 2 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8275 (Proxy)	6.65 ms	0 - 99 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8550 (Proxy)	3.44 ms	0 - 130 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS9075	3.795 ms	0 - 2 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCM6690	16.082 ms	0 - 231 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Qualcomm® QCS8450 (Proxy)	4.127 ms	0 - 154 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	1.615 ms	0 - 103 MB	NPU
EfficientNet-B4	QNN_DLC	w8a16	Snapdragon® 7 Gen 4 Mobile	3.622 ms	0 - 105 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	1.506 ms	0 - 105 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	2.407 ms	0 - 166 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8275 (Proxy)	12.08 ms	0 - 106 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8550 (Proxy)	3.35 ms	0 - 3 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS9075	4.193 ms	0 - 48 MB	NPU
EfficientNet-B4	TFLITE	float	Qualcomm® QCS8450 (Proxy)	7.854 ms	0 - 189 MB	NPU
EfficientNet-B4	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	1.854 ms	0 - 109 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.