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 1.24.1 | Download |
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | Download |
| QNN_DLC | float | Universal | QAIRT 2.43 | Download |
| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | Download |
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | Download |
| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.17.0 | 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 | 0.423 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® X2 Elite | 0.423 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® X Elite | 0.954 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) | 0.811 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS9075 | 1.005 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.432 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.338 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 | 0.694 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.409 ms | 0 - 37 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS6490 | 3.522 ms | 4 - 7 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.57 ms | 0 - 4 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.677 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCM6690 | 2.21 ms | 0 - 8 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.352 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.439 ms | 0 - 9 MB | CPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.286 ms | 1 - 31 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X2 Elite | 0.411 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X Elite | 0.946 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.509 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1.743 ms | 1 - 29 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.787 ms | 1 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8775P | 1.041 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS9075 | 0.886 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.362 ms | 0 - 41 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA7255P | 1.743 ms | 1 - 29 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8295P | 1.242 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.37 ms | 1 - 28 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.211 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.311 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.597 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.332 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.168 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.063 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.471 ms | 0 - 71 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.655 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.568 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.349 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.544 ms | 0 - 33 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.063 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.839 ms | 0 - 20 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.263 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.465 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.29 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.507 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 1.778 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.792 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8775P | 1.037 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS9075 | 0.893 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.378 ms | 0 - 40 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA7255P | 1.778 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8295P | 1.255 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.371 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.25 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.308 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS6490 | 0.894 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.031 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.451 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.643 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.587 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.072 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.513 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.031 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.787 ms | 0 - 20 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.267 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.44 ms | 0 - 21 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.
