SwinV2-Base: Optimized for Qualcomm Devices

SwinV2Base 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 SwinV2-Base 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
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 SwinV2-Base 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 SwinV2-Base on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

• Model checkpoint: Imagenet
• Input resolution: 256x256
• Number of parameters: 88.8M
• Model size (float): 339 MB
• Model size (w8a16): 90.2 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
SwinV2-Base	QNN_DLC	float	Snapdragon® X2 Elite	12.451 ms	1 - 1 MB	NPU
SwinV2-Base	QNN_DLC	float	Snapdragon® X Elite	28.955 ms	1 - 1 MB	NPU
SwinV2-Base	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	19.714 ms	1 - 545 MB	NPU
SwinV2-Base	QNN_DLC	float	Snapdragon® 8 Gen 1 Mobile	41.129 ms	0 - 534 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8275	74.071 ms	1 - 395 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	27.948 ms	1 - 449 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® SA8775P	31.464 ms	1 - 388 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® SA8650P	31.464 ms	1 - 388 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® SA8255P	31.464 ms	1 - 388 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® QCS8450	41.129 ms	0 - 534 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-9075	39.558 ms	3 - 5 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-X7181	28.955 ms	1 - 1 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ Q-8750	14.613 ms	1 - 392 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® SA7255P	74.071 ms	1 - 395 MB	NPU
SwinV2-Base	QNN_DLC	float	Qualcomm® SA8295P	37.795 ms	1 - 378 MB	NPU
SwinV2-Base	QNN_DLC	float	Snapdragon® 8 Elite Mobile	14.613 ms	1 - 392 MB	NPU
SwinV2-Base	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	11.583 ms	0 - 424 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Snapdragon® X2 Elite	12.215 ms	0 - 0 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Snapdragon® X Elite	30.702 ms	0 - 0 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Snapdragon® 8 Gen 3 Mobile	19.654 ms	0 - 2036 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® Dragonwing™ QCS8275	52.667 ms	0 - 486 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® Dragonwing™ QCS8550 (Proxy)	29.334 ms	0 - 3 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® SA8775P	29.997 ms	0 - 869 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® SA8650P	29.997 ms	0 - 869 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® SA8255P	29.997 ms	0 - 869 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® Dragonwing™ IQ-9075	29.947 ms	0 - 2 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® Dragonwing™ IQ-X7181	30.702 ms	0 - 0 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® Dragonwing™ Q-8750	14.736 ms	0 - 903 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Qualcomm® SA7255P	52.667 ms	0 - 486 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Snapdragon® 8 Elite Mobile	14.736 ms	0 - 903 MB	NPU
SwinV2-Base	QNN_DLC	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	11.415 ms	0 - 953 MB	NPU
SwinV2-Base	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	19.963 ms	0 - 2174 MB	NPU
SwinV2-Base	TFLITE	float	Snapdragon® 8 Gen 1 Mobile	42.639 ms	0 - 676 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® Dragonwing™ QCS8275	71.105 ms	0 - 885 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	29.556 ms	0 - 4 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® SA8775P	32.555 ms	0 - 879 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® SA8650P	32.555 ms	0 - 879 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® SA8255P	32.555 ms	0 - 879 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® QCS8450	42.639 ms	0 - 676 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® Dragonwing™ IQ-9075	32.565 ms	0 - 181 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® Dragonwing™ Q-8750	14.878 ms	0 - 896 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® SA7255P	71.105 ms	0 - 885 MB	NPU
SwinV2-Base	TFLITE	float	Qualcomm® SA8295P	40.584 ms	0 - 875 MB	NPU
SwinV2-Base	TFLITE	float	Snapdragon® 8 Elite Mobile	14.878 ms	0 - 896 MB	NPU
SwinV2-Base	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	11.386 ms	0 - 939 MB	NPU

License

• The license for the original implementation of SwinV2-Base can be found here.

References

• Swin Transformer V2: Scaling Up Capacity and Resolution
• 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.