MNASNet05: Optimized for Qualcomm Devices
MNASNet05 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 MNASNet05 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 | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | Download |
| QNN_DLC | float | Universal | QAIRT 2.45 | Download |
| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | Download |
| TFLITE | float | Universal | QAIRT 2.45 | Download |
For more device-specific assets and performance metrics, visit MNASNet05 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 MNASNet05 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: 2.21M
- Model size (float): 8.45 MB
- Model size (w8a16): 2.79 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.218 ms | 0 - 32 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Mobile | 0.266 ms | 0 - 27 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® X2 Elite | 0.255 ms | 5 - 5 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.613 ms | 5 - 5 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.613 ms | 5 - 5 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.338 ms | 0 - 47 MB | NPU |
| MNASNet05 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.487 ms | 0 - 2 MB | NPU |
| MNASNet05 | ONNX | float | Qualcomm® QCS9075 | 0.759 ms | 1 - 3 MB | NPU |
| MNASNet05 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.266 ms | 0 - 27 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.215 ms | 0 - 33 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 0.263 ms | 0 - 33 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® X2 Elite | 0.231 ms | 0 - 0 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.349 ms | 0 - 39 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS6490 | 23.765 ms | 10 - 14 MB | CPU |
| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.517 ms | 0 - 5 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS9075 | 0.69 ms | 0 - 3 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Qualcomm® QCM6690 | 10.309 ms | 12 - 19 MB | CPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.263 ms | 0 - 33 MB | NPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 7.685 ms | 12 - 20 MB | CPU |
| MNASNet05 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 7.685 ms | 12 - 20 MB | CPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.291 ms | 1 - 33 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 0.384 ms | 0 - 32 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® X2 Elite | 0.402 ms | 1 - 1 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® X Elite | 0.944 ms | 1 - 1 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® X Elite | 0.944 ms | 1 - 1 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.512 ms | 0 - 45 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 2.303 ms | 1 - 27 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.787 ms | 1 - 2 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.113 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.113 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.113 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS9075 | 0.98 ms | 3 - 5 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.571 ms | 1 - 47 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® SA7255P | 2.303 ms | 1 - 27 MB | NPU |
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8295P | 1.416 ms | 0 - 28 MB | NPU |
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.384 ms | 0 - 32 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.301 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 0.355 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 0.408 ms | 0 - 0 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.922 ms | 0 - 0 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.922 ms | 0 - 0 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.528 ms | 0 - 37 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 2.181 ms | 0 - 2 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 1.664 ms | 0 - 26 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.77 ms | 0 - 2 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.95 ms | 0 - 28 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.95 ms | 0 - 28 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.95 ms | 0 - 28 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 0.922 ms | 0 - 2 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 3.052 ms | 0 - 139 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 0.961 ms | 0 - 40 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.664 ms | 0 - 26 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.234 ms | 0 - 24 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.355 ms | 0 - 30 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.786 ms | 0 - 25 MB | NPU |
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.786 ms | 0 - 25 MB | NPU |
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.292 ms | 0 - 32 MB | NPU |
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Mobile | 0.381 ms | 0 - 33 MB | NPU |
| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.523 ms | 0 - 46 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 2.32 ms | 0 - 28 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.796 ms | 0 - 9 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 1.126 ms | 0 - 31 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 1.126 ms | 0 - 31 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 1.126 ms | 0 - 31 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® QCS9075 | 0.99 ms | 0 - 8 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.577 ms | 0 - 48 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® SA7255P | 2.32 ms | 0 - 28 MB | NPU |
| MNASNet05 | TFLITE | float | Qualcomm® SA8295P | 1.446 ms | 0 - 28 MB | NPU |
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.381 ms | 0 - 33 MB | NPU |
License
- The license for the original implementation of MNASNet05 can be found here.
References
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.
