EfficientNet-B0: Optimized for Qualcomm Devices

EfficientNetB0 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-B0 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
ONNX	w8a16	Universal	QAIRT 2.42, ONNX Runtime 1.24.3	Download
QNN_DLC	float	Universal	QAIRT 2.45	Download
QNN_DLC	w8a16	Universal	QAIRT 2.45	Download
TFLITE	float	Universal	QAIRT 2.45	Download

For more device-specific assets and performance metrics, visit EfficientNet-B0 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-B0 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: 5.27M
Model size (float): 20.1 MB
Model size (w8a16): 6.99 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
EfficientNet-B0	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	0.546 ms	0 - 43 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® X2 Elite	0.671 ms	13 - 13 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® X Elite	1.46 ms	13 - 13 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® 8 Gen 3 Mobile	0.898 ms	0 - 73 MB	NPU
EfficientNet-B0	ONNX	float	Qualcomm® QCS8550 (Proxy)	1.268 ms	0 - 16 MB	NPU
EfficientNet-B0	ONNX	float	Qualcomm® QCS9075	1.63 ms	1 - 3 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	0.696 ms	0 - 38 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.557 ms	0 - 58 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® X2 Elite	0.597 ms	6 - 6 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® X Elite	1.649 ms	6 - 6 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Gen 3 Mobile	0.954 ms	0 - 84 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS6490	113.782 ms	46 - 49 MB	CPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS8550 (Proxy)	1.418 ms	0 - 9 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS9075	1.612 ms	0 - 3 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCM6690	49.03 ms	41 - 50 MB	CPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.67 ms	0 - 52 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 7 Gen 4 Mobile	37.92 ms	42 - 51 MB	CPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	0.598 ms	1 - 41 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® X2 Elite	0.837 ms	1 - 1 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® X Elite	1.777 ms	1 - 1 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	1.075 ms	0 - 60 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	4.917 ms	1 - 36 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	1.556 ms	1 - 89 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA8775P	2.014 ms	1 - 39 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS9075	1.887 ms	1 - 3 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	3.618 ms	0 - 70 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA7255P	4.917 ms	1 - 36 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA8295P	3.604 ms	0 - 43 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	0.805 ms	1 - 37 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.638 ms	0 - 50 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® X2 Elite	0.807 ms	0 - 0 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® X Elite	1.891 ms	0 - 0 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	1.114 ms	0 - 63 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS6490	4.038 ms	0 - 2 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8275 (Proxy)	3.238 ms	0 - 47 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8550 (Proxy)	1.667 ms	0 - 2 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA8775P	1.941 ms	0 - 49 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS9075	1.857 ms	2 - 4 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCM6690	6.497 ms	0 - 163 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8450 (Proxy)	1.98 ms	0 - 66 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA7255P	3.238 ms	0 - 47 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA8295P	2.362 ms	0 - 44 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.771 ms	0 - 50 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 7 Gen 4 Mobile	1.7 ms	0 - 47 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	0.597 ms	0 - 46 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	1.07 ms	0 - 66 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8275 (Proxy)	4.924 ms	0 - 41 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8550 (Proxy)	1.542 ms	0 - 6 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA8775P	2.031 ms	0 - 45 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS9075	1.886 ms	0 - 16 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8450 (Proxy)	3.618 ms	0 - 75 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA7255P	4.924 ms	0 - 41 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA8295P	3.66 ms	0 - 48 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	0.815 ms	0 - 41 MB	NPU

License

The license for the original implementation of EfficientNet-B0 can be found here.

References

Community

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Paper for qualcomm/EfficientNet-B0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 4