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 2.45, ONNX Runtime 1.25.0 | Download |
| ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.25.0 | 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® X2 Elite | 0.435 ms | 180 - 180 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® X Elite | 0.843 ms | 178 - 178 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.519 ms | 0 - 29 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 0.962 ms | 1 - 36 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.825 ms | 0 - 7 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS8450 | 0.962 ms | 1 - 36 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite Mobile | 0.447 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.437 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS9075 | 1.002 ms | 0 - 48 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS8750 | 0.447 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | ONNX | float | Qualcomm® QCS7181 | 0.843 ms | 178 - 178 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® X2 Elite | 0.328 ms | 213 - 213 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® X Elite | 0.577 ms | 177 - 177 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.395 ms | 0 - 29 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Gen 1 Mobile | 0.639 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS6490 | 1.108 ms | 0 - 47 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.579 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8450 | 0.639 ms | 0 - 30 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.341 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.526 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCM6690 | 1.223 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS9075 | 0.692 ms | 0 - 47 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Snapdragon® 8 Elite Mobile | 0.355 ms | 0 - 35 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS7790 | 0.526 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS8750 | 0.355 ms | 0 - 35 MB | NPU |
| Shufflenet-v2 | ONNX | w8a8 | Qualcomm® QCS7181 | 0.577 ms | 177 - 177 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X2 Elite | 0.395 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® X Elite | 0.93 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.513 ms | 0 - 38 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 1.349 ms | 0 - 44 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8275 | 1.735 ms | 1 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.788 ms | 0 - 8 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8450 | 1.349 ms | 0 - 44 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 0.363 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA8295P | 1.239 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.277 ms | 1 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® SA7255P | 1.735 ms | 1 - 27 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS9075 | 0.882 ms | 3 - 5 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS8750 | 0.363 ms | 0 - 26 MB | NPU |
| Shufflenet-v2 | QNN_DLC | float | Qualcomm® QCS7181 | 0.93 ms | 1 - 1 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 0.304 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.586 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.335 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 1 Mobile | 0.543 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.144 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8275 | 1.081 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.47 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8450 | 0.543 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.213 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.461 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.349 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.563 ms | 2 - 4 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.081 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Mobile | 0.263 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.834 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS7790 | 0.461 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8750 | 0.263 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS7181 | 0.586 ms | 0 - 0 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.498 ms | 0 - 38 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 1.361 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8275 | 1.723 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.779 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8775P | 1.977 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8650P | 1.977 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8255P | 1.977 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8450 | 1.361 ms | 0 - 39 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite Mobile | 0.368 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA8295P | 1.243 ms | 0 - 24 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.284 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® SA7255P | 1.723 ms | 0 - 27 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS9075 | 0.892 ms | 0 - 5 MB | NPU |
| Shufflenet-v2 | TFLITE | float | Qualcomm® QCS8750 | 0.368 ms | 0 - 25 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.315 ms | 0 - 32 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 1 Mobile | 0.512 ms | 0 - 38 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS6490 | 0.815 ms | 0 - 4 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8275 | 1.032 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.455 ms | 0 - 2 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8775P | 1.918 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8650P | 1.918 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8255P | 1.918 ms | 0 - 24 MB | CPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8450 | 0.512 ms | 0 - 38 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.259 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.442 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.072 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.558 ms | 0 - 3 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA7255P | 1.032 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Mobile | 0.271 ms | 0 - 23 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.808 ms | 0 - 21 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS7790 | 0.442 ms | 0 - 22 MB | NPU |
| Shufflenet-v2 | TFLITE | w8a8 | Qualcomm® QCS8750 | 0.271 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.
