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 2.37, ONNX Runtime 1.23.0 | Download |
| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | Download |
| QNN_DLC | float | Universal | QAIRT 2.42 | Download |
| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | Download |
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | Download |
| TFLITE | w8a8 | Universal | QAIRT 2.42, 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® X Elite | 0.94 ms | 2 - 2 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.603 ms | 0 - 107 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.991 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS9075 | 1.201 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.544 ms | 0 - 95 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.537 ms | 0 - 99 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® X Elite | 0.684 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.572 ms | 0 - 105 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS6490 | 3.021 ms | 3 - 7 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.728 ms | 0 - 4 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.885 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCM6690 | 1.935 ms | 0 - 8 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.466 ms | 0 - 99 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.252 ms | 0 - 9 MB | CPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.436 ms | 0 - 99 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X Elite | 0.895 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.487 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1.691 ms | 0 - 29 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.754 ms | 1 - 7 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8775P | 0.985 ms | 1 - 30 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS9075 | 0.882 ms | 1 - 3 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.359 ms | 0 - 40 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA7255P | 1.691 ms | 0 - 29 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8295P | 1.229 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.354 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.284 ms | 1 - 31 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.604 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.336 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.172 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.083 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.477 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.643 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.561 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.334 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.545 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.083 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.838 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.262 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.464 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.204 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.494 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 1.706 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.76 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8775P | 4.146 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS9075 | 0.887 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.37 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA7255P | 1.706 ms | 0 - 28 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8295P | 1.225 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.366 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.282 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.311 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS6490 | 0.803 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.105 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.45 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.636 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.558 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.055 ms | 0 - 20 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.515 ms | 0 - 31 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.105 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.798 ms | 0 - 20 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.27 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.44 ms | 0 - 20 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.25 ms | 0 - 25 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.
