ConvNextV2-Base: Optimized for Qualcomm Devices

ConvNextV2Base 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 ConvNextV2-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
ONNX	float	Universal	QAIRT 2.50, ONNX Runtime 1.30.0	Download
ONNX	w8a16	Universal	QAIRT 2.50, ONNX Runtime 1.30.0	Download
QNN_DLC	float	Universal	QAIRT 2.50	Download
TFLITE	float	Universal	QAIRT 2.50	Download

For more device-specific assets and performance metrics, visit ConvNextV2-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 ConvNextV2-Base on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

• Input resolution: 224x224
• Model checkpoint: Imagenet
• Model size (float): 339 MB
• Model size (w8a16): 91.7 MB
• Number of parameters: 88.7M

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	7.536 ms	0 - 376 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	11.997 ms	1 - 372 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® X2 Elite	7.957 ms	2 - 2 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® X Elite	17.84 ms	183 - 183 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Gen 3 Mobile	13.038 ms	0 - 552 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Gen 1 Mobile	28.631 ms	1 - 356 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® Dragonwing™ IQ-8275	21.163 ms	0 - 5 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	17.748 ms	0 - 194 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® QCS8450	28.631 ms	1 - 356 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® Dragonwing™ IQ-9075	20.751 ms	0 - 4 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® Dragonwing™ IQ-X7181	17.84 ms	183 - 183 MB	NPU
ConvNextV2-Base	ONNX	float	Qualcomm® Dragonwing™ Q-8750	11.997 ms	1 - 372 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Elite Extreme Gen 6 Mobile	6.922 ms	1 - 379 MB	NPU
ConvNextV2-Base	ONNX	float	Snapdragon® 8 Elite Gen 6 Mobile	8.756 ms	0 - 408 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	5.646 ms	0 - 285 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	9.614 ms	0 - 261 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® X2 Elite	6.006 ms	1 - 1 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® X Elite	14.205 ms	92 - 92 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Gen 3 Mobile	9.801 ms	0 - 336 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Gen 1 Mobile	17.584 ms	0 - 331 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® Dragonwing™ IQ-8275	13.028 ms	0 - 5 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® Dragonwing™ QCS8550 (Proxy)	13.572 ms	0 - 99 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® QCS8450	17.584 ms	0 - 331 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® Dragonwing™ IQ-9075	14.097 ms	0 - 4 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® Dragonwing™ IQ-X7181	14.205 ms	92 - 92 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Qualcomm® Dragonwing™ Q-8750	9.614 ms	0 - 261 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Elite Extreme Gen 6 Mobile	4.627 ms	0 - 305 MB	NPU
ConvNextV2-Base	ONNX	w8a16	Snapdragon® 8 Elite Gen 6 Mobile	6.346 ms	0 - 289 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	7.64 ms	0 - 369 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	12.17 ms	1 - 364 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® X2 Elite	8.538 ms	1 - 1 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® X Elite	18.505 ms	1 - 1 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	13.285 ms	1 - 543 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Gen 1 Mobile	28.813 ms	0 - 342 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	21.142 ms	1 - 4 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	17.955 ms	1 - 3 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® SA8775P	21.476 ms	1 - 397 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® SA8650P	21.476 ms	1 - 397 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® SA8255P	21.476 ms	1 - 397 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® QCS8450	28.813 ms	0 - 342 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-9075	20.923 ms	1 - 3 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-X7181	18.505 ms	1 - 1 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® Dragonwing™ Q-8750	12.17 ms	1 - 364 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® SA7255P	57.693 ms	1 - 397 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Qualcomm® SA8295P	30.498 ms	1 - 209 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Elite Extreme Gen 6 Mobile	6.938 ms	1 - 370 MB	NPU
ConvNextV2-Base	QNN_DLC	float	Snapdragon® 8 Elite Gen 6 Mobile	8.895 ms	1 - 401 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Elite Gen 5 For Galaxy Mobile	5.383 ms	0 - 324 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	8.039 ms	0 - 300 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	8.229 ms	0 - 453 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Gen 1 Mobile	26.29 ms	0 - 361 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	15.814 ms	0 - 180 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	11.85 ms	0 - 3 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® SA8775P	15.307 ms	0 - 301 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® SA8650P	15.307 ms	0 - 301 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® SA8255P	15.307 ms	0 - 301 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® QCS8450	26.29 ms	0 - 361 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® Dragonwing™ IQ-9075	15.882 ms	0 - 179 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® Dragonwing™ Q-8750	8.039 ms	0 - 300 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® SA7255P	47.766 ms	0 - 301 MB	NPU
ConvNextV2-Base	TFLITE	float	Qualcomm® SA8295P	26.265 ms	0 - 221 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Elite Extreme Gen 6 Mobile	4.675 ms	0 - 320 MB	NPU
ConvNextV2-Base	TFLITE	float	Snapdragon® 8 Elite Gen 6 Mobile	5.191 ms	0 - 309 MB	NPU

License

• The license for the original implementation of ConvNextV2-Base can be found here.

References

• ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
• 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.