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 1.24.1	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-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® X Elite	1.456 ms	13 - 13 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® 8 Gen 3 Mobile	0.899 ms	0 - 65 MB	NPU
EfficientNet-B0	ONNX	float	Qualcomm® QCS8550 (Proxy)	1.267 ms	0 - 15 MB	NPU
EfficientNet-B0	ONNX	float	Qualcomm® QCS9075	1.629 ms	1 - 3 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	0.696 ms	0 - 37 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	0.546 ms	0 - 43 MB	NPU
EfficientNet-B0	ONNX	float	Snapdragon® X2 Elite	0.667 ms	13 - 13 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® X Elite	1.636 ms	6 - 6 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Gen 3 Mobile	0.947 ms	0 - 85 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS6490	113.227 ms	43 - 46 MB	CPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS8550 (Proxy)	1.419 ms	0 - 10 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCS9075	1.621 ms	0 - 3 MB	NPU
EfficientNet-B0	ONNX	w8a16	Qualcomm® QCM6690	48.896 ms	41 - 51 MB	CPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.671 ms	0 - 61 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 7 Gen 4 Mobile	39.043 ms	42 - 51 MB	CPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.559 ms	0 - 58 MB	NPU
EfficientNet-B0	ONNX	w8a16	Snapdragon® X2 Elite	0.579 ms	6 - 6 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® X Elite	1.766 ms	1 - 1 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	1.078 ms	0 - 63 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	4.93 ms	1 - 38 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	1.57 ms	1 - 2 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA8775P	2.051 ms	0 - 43 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS9075	1.868 ms	1 - 3 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	3.624 ms	0 - 74 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA7255P	4.93 ms	1 - 38 MB	NPU
EfficientNet-B0	QNN_DLC	float	Qualcomm® SA8295P	3.65 ms	1 - 46 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	0.818 ms	1 - 40 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	0.607 ms	1 - 44 MB	NPU
EfficientNet-B0	QNN_DLC	float	Snapdragon® X2 Elite	0.879 ms	1 - 1 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® X Elite	1.924 ms	0 - 0 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	1.147 ms	0 - 65 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS6490	4.115 ms	2 - 4 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8275 (Proxy)	3.327 ms	0 - 45 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8550 (Proxy)	1.692 ms	0 - 2 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA8775P	1.969 ms	0 - 49 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS9075	1.857 ms	0 - 2 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCM6690	6.462 ms	0 - 163 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® QCS8450 (Proxy)	1.951 ms	0 - 67 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA7255P	3.327 ms	0 - 45 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Qualcomm® SA8295P	2.419 ms	0 - 43 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	0.785 ms	0 - 45 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 7 Gen 4 Mobile	1.72 ms	0 - 46 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	0.642 ms	0 - 48 MB	NPU
EfficientNet-B0	QNN_DLC	w8a16	Snapdragon® X2 Elite	0.816 ms	0 - 0 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	1.077 ms	0 - 77 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8275 (Proxy)	4.939 ms	0 - 46 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8550 (Proxy)	1.568 ms	0 - 6 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA8775P	2.047 ms	0 - 49 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS9075	1.876 ms	0 - 16 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® QCS8450 (Proxy)	3.634 ms	0 - 82 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA7255P	4.939 ms	0 - 46 MB	NPU
EfficientNet-B0	TFLITE	float	Qualcomm® SA8295P	3.683 ms	0 - 52 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	0.824 ms	0 - 51 MB	NPU
EfficientNet-B0	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	0.609 ms	0 - 50 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 • 3