v0.49.1
Browse filesSee https://github.com/qualcomm/ai-hub-models/releases/v0.49.1 for changelog.
README.md
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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.
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This is based on the implementation of MNASNet05 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py).
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This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/
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Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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### Option 1: Download Pre-Exported Models
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| Runtime | Precision | Chipset | SDK Versions | Download |
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| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mnasnet05/releases/v0.48.0/mnasnet05-onnx-float.zip)
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| ONNX | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mnasnet05/releases/v0.48.0/mnasnet05-onnx-w8a16.zip)
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| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mnasnet05/releases/v0.48.0/mnasnet05-qnn_dlc-float.zip)
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| QNN_DLC | w8a16 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mnasnet05/releases/v0.48.0/mnasnet05-qnn_dlc-w8a16.zip)
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| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mnasnet05/releases/v0.48.0/mnasnet05-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[MNASNet05 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/mnasnet05)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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See our repository for [MNASNet05 on GitHub](https://github.com/qualcomm/ai-hub-models/
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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| MNASNet05 | ONNX | float | Snapdragon® X2 Elite | 0.227 ms | 5 - 5 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.609 ms | 5 - 5 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.335 ms | 0 - 47 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.49 ms | 0 - 2 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS9075 | 0.761 ms | 1 - 3 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.266 ms | 0 - 28 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.217 ms | 0 - 32 MB | NPU
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX |
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| MNASNet05 | ONNX | w8a16 |
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| MNASNet05 | ONNX | w8a16 | Snapdragon®
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| MNASNet05 | ONNX | w8a16 | Snapdragon®
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC | float |
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| MNASNet05 | QNN_DLC |
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| MNASNet05 | QNN_DLC |
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.527 ms | 0 - 36 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 2.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 1.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 0.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 3.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 0.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.795 ms | 0 - 25 MB | NPU
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 2.342 ms | 0 - 30 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.
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| MNASNet05 | TFLITE | float | Qualcomm® SA8775P |
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| MNASNet05 | TFLITE | float | Qualcomm® QCS9075 | 0.
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.
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| MNASNet05 | TFLITE | float | Qualcomm® SA7255P | 2.342 ms | 0 - 30 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® SA8295P | 1.
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.398 ms | 0 - 30 MB | NPU
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.306 ms | 0 - 33 MB | NPU
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## License
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* The license for the original implementation of MNASNet05 can be found
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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.
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This is based on the implementation of MNASNet05 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py).
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This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/mnasnet05) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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### Option 1: Download Pre-Exported Models
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Download pre-exported model assets from **[MNASNet05 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/mnasnet05)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/mnasnet05) Python library to compile and export the model with your own:
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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See our repository for [MNASNet05 on GitHub](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/mnasnet05) for usage instructions.
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.217 ms | 0 - 32 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® X2 Elite | 0.232 ms | 5 - 5 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.621 ms | 5 - 5 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.337 ms | 0 - 47 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.488 ms | 0 - 2 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS9075 | 0.762 ms | 1 - 3 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.28 ms | 0 - 28 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.217 ms | 0 - 32 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® X2 Elite | 0.216 ms | 0 - 0 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® X Elite | 0.644 ms | 2 - 2 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.344 ms | 0 - 40 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS6490 | 29.144 ms | 10 - 13 MB | CPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.519 ms | 0 - 7 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS9075 | 0.711 ms | 0 - 3 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCM6690 | 10.406 ms | 9 - 17 MB | CPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.261 ms | 0 - 27 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 7.673 ms | 10 - 18 MB | CPU
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| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.301 ms | 1 - 33 MB | NPU
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| MNASNet05 | QNN_DLC | float | Snapdragon® X2 Elite | 0.446 ms | 1 - 1 MB | NPU
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| MNASNet05 | QNN_DLC | float | Snapdragon® X Elite | 0.989 ms | 1 - 1 MB | NPU
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| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.522 ms | 0 - 46 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 2.331 ms | 1 - 29 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.821 ms | 1 - 7 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.121 ms | 1 - 31 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® QCS9075 | 0.979 ms | 3 - 5 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.573 ms | 0 - 48 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® SA7255P | 2.331 ms | 1 - 29 MB | NPU
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| MNASNet05 | QNN_DLC | float | Qualcomm® SA8295P | 1.443 ms | 1 - 29 MB | NPU
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| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.401 ms | 0 - 34 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.296 ms | 0 - 28 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 0.422 ms | 0 - 0 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.941 ms | 0 - 0 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.527 ms | 0 - 36 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 2.252 ms | 0 - 2 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 1.675 ms | 0 - 26 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.787 ms | 0 - 2 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.992 ms | 0 - 27 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 0.923 ms | 0 - 2 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 3.035 ms | 0 - 138 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 0.951 ms | 0 - 38 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.675 ms | 0 - 26 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.247 ms | 0 - 23 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.363 ms | 0 - 25 MB | NPU
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| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.795 ms | 0 - 25 MB | NPU
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.306 ms | 0 - 34 MB | NPU
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.531 ms | 0 - 47 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 2.342 ms | 0 - 30 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.801 ms | 0 - 9 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 4.757 ms | 0 - 31 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® QCS9075 | 0.987 ms | 0 - 8 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.587 ms | 0 - 49 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® SA7255P | 2.342 ms | 0 - 30 MB | NPU
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| MNASNet05 | TFLITE | float | Qualcomm® SA8295P | 1.455 ms | 0 - 29 MB | NPU
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| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.398 ms | 0 - 30 MB | NPU
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## License
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* The license for the original implementation of MNASNet05 can be found
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