EfficientViT-l2-cls: Optimized for Qualcomm Devices

EfficientViT 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 EfficientViT-l2-cls 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
TFLITE	float	Universal	QAIRT 2.43, TFLite 2.17.0	Download

For more device-specific assets and performance metrics, visit EfficientViT-l2-cls 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 EfficientViT-l2-cls 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: 63.7M
• Model size (float): 243 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
EfficientViT-l2-cls	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	3.219 ms	1 - 159 MB	NPU
EfficientViT-l2-cls	ONNX	float	Snapdragon® X2 Elite	3.519 ms	131 - 131 MB	NPU
EfficientViT-l2-cls	ONNX	float	Snapdragon® X Elite	7.978 ms	131 - 131 MB	NPU
EfficientViT-l2-cls	ONNX	float	Snapdragon® 8 Gen 3 Mobile	5.199 ms	0 - 249 MB	NPU
EfficientViT-l2-cls	ONNX	float	Qualcomm® QCS8550 (Proxy)	7.294 ms	0 - 162 MB	NPU
EfficientViT-l2-cls	ONNX	float	Qualcomm® QCS9075	8.398 ms	0 - 4 MB	NPU
EfficientViT-l2-cls	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	3.926 ms	0 - 130 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	3.21 ms	1 - 143 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Snapdragon® X2 Elite	4.012 ms	1 - 1 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Snapdragon® X Elite	7.91 ms	1 - 1 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	5.317 ms	0 - 236 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	24.281 ms	1 - 139 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	7.254 ms	1 - 3 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Qualcomm® QCS9075	8.566 ms	3 - 5 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	14.844 ms	1 - 221 MB	NPU
EfficientViT-l2-cls	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	3.949 ms	0 - 147 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	3.197 ms	0 - 280 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	5.262 ms	0 - 360 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Qualcomm® QCS8275 (Proxy)	24.294 ms	0 - 274 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Qualcomm® QCS8550 (Proxy)	6.983 ms	0 - 3 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Qualcomm® QCS9075	8.501 ms	0 - 134 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Qualcomm® QCS8450 (Proxy)	14.764 ms	0 - 360 MB	NPU
EfficientViT-l2-cls	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	3.916 ms	0 - 267 MB	NPU

License

• The license for the original implementation of EfficientViT-l2-cls can be found here.

References

• EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
• 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.