Shufflenet-v2: Optimized for Qualcomm Devices
ShufflenetV2 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 Shufflenet-v2 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 | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | Download |
| QNN_DLC | float | Universal | QAIRT 2.45 | Download |
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | Download |
| TFLITE | float | Universal | QAIRT 2.45 | Download |
| TFLITE | w8a8 | Universal | QAIRT 2.45 | Download |
For more device-specific assets and performance metrics, visit Shufflenet-v2 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 Shufflenet-v2 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: 1.37M
- Model size (float): 5.24 MB
- Model size (w8a8): 1.47 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.426 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® X2 Elite | 0.425 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® X Elite | 0.966 ms | 2 - 2 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.51 ms | 0 - 41 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.819 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS9075 | 0.995 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.434 ms | 0 - 33 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.34 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® X2 Elite | 0.332 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® X Elite | 0.709 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.411 ms | 0 - 37 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS6490 | 3.231 ms | 4 - 7 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.576 ms | 0 - 13 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.686 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCM6690 | 2.196 ms | 0 - 9 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.356 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.478 ms | 0 - 9 MB | CPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.279 ms | 1 - 31 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X2 Elite | 0.393 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X Elite | 0.951 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.504 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1.728 ms | 1 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.783 ms | 1 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8775P | 1.003 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS9075 | 0.883 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.36 ms | 0 - 40 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA7255P | 1.728 ms | 1 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8295P | 1.247 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.361 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.21 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.307 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.598 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.338 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.171 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.068 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.47 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.641 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.56 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.333 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.541 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.068 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.826 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.26 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.459 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.281 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.499 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 1.736 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.777 ms | 0 - 18 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8775P | 1.02 ms | 0 - 29 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS9075 | 0.887 ms | 0 - 6 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.367 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA7255P | 1.736 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8295P | 1.255 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.367 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.252 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.321 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS6490 | 0.875 ms | 0 - 4 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.038 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.456 ms | 0 - 1 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.651 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.568 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.061 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.512 ms | 0 - 33 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.038 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.821 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.273 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.442 ms | 0 - 23 MB | NPU |
License
- The license for the original implementation of Shufflenet-v2 can be found here.
References
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- 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.
