---
title: MNASNet05
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/MNASNet05"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/MNASNet05.md"
repository: Qualcomm-AI-Hub/MNASNet05
last_updated: 2026-10-16
license: bsd-3-clause
pipeline_tag: image-classification
tasks:
  - image-classification
downloads: 0
stars: 0
tags:
  - backbone
  - bu_auto
  - android
---

# MNASNet05

> MNASNet05 - Qualcomm-AI-Hub 在 ModelScope 开源的模型。MNASNet05: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/MNASNet05 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。

- **Repository**: Qualcomm-AI-Hub/MNASNet05
- **License**: bsd-3-clause
- **Tasks**: image-classification
- **Tags**: backbone, bu_auto, android
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/MNASNet05

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/web-assets/model_demo.png)

# 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](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py).
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-cn/blob/main/src/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).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.cn) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) 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 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/releases/v0.62.1/mnasnet05-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/releases/v0.62.1/mnasnet05-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/releases/v0.62.1/mnasnet05-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/releases/v0.62.1/mnasnet05-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/mnasnet05/releases/v0.62.1/mnasnet05-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[MNASNet05 on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/mnasnet05)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/mnasnet05) 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](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/mnasnet05) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Input resolution: 224x224
- Model checkpoint: Imagenet
- Model size (float): 8.45 MB
- Model size (w8a16): 2.79 MB
- Number of parameters: 2.21M

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| MNASNet05 | ONNX | float | Snapdragon® X2 Elite | 0.235 ms | 2 - 2 MB | NPU
| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.484 ms | 5 - 5 MB | NPU
| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.332 ms | 0 - 47 MB | NPU
| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 0.872 ms | 1 - 52 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 0.7 ms | 0 - 4 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.487 ms | 0 - 7 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® QCS8450 | 0.872 ms | 1 - 52 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 0.704 ms | 1 - 3 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 0.484 ms | 5 - 5 MB | NPU
| MNASNet05 | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 0.269 ms | 0 - 28 MB | NPU
| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Mobile | 0.269 ms | 0 - 28 MB | NPU
| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.231 ms | 0 - 29 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® X2 Elite | 0.219 ms | 1 - 1 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® X Elite | 0.515 ms | 0 - 0 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.345 ms | 0 - 44 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 0.669 ms | 0 - 47 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 2.163 ms | 0 - 3 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 0.754 ms | 0 - 4 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.515 ms | 0 - 5 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS8450 | 0.669 ms | 0 - 47 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 0.652 ms | 0 - 3 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 0.515 ms | 0 - 0 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 2.239 ms | 0 - 145 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 0.536 ms | 0 - 31 MB | NPU
| MNASNet05 | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 0.261 ms | 0 - 30 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 0.261 ms | 0 - 30 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.219 ms | 0 - 33 MB | NPU
| MNASNet05 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.536 ms | 0 - 31 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® X2 Elite | 0.402 ms | 1 - 1 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® X Elite | 0.915 ms | 1 - 1 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.509 ms | 0 - 45 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 1.564 ms | 0 - 51 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 0.906 ms | 1 - 4 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.788 ms | 0 - 3 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.103 ms | 1 - 30 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8650P | 1.103 ms | 1 - 30 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8255P | 1.103 ms | 1 - 30 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8450 | 1.564 ms | 0 - 51 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 0.988 ms | 1 - 3 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 0.915 ms | 1 - 1 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 0.385 ms | 1 - 32 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® SA7255P | 2.305 ms | 1 - 28 MB | NPU
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8295P | 1.418 ms | 0 - 27 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 0.385 ms | 1 - 32 MB | NPU
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.295 ms | 1 - 29 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 0.408 ms | 0 - 0 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.907 ms | 0 - 0 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.517 ms | 0 - 38 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 1 Mobile | 0.956 ms | 0 - 45 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 2.747 ms | 2 - 4 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 0.768 ms | 0 - 3 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.764 ms | 0 - 23 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 0.951 ms | 0 - 29 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8650P | 0.951 ms | 0 - 29 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8255P | 0.951 ms | 0 - 29 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8450 | 0.956 ms | 0 - 45 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 0.853 ms | 0 - 2 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 0.907 ms | 0 - 0 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 3.047 ms | 0 - 140 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 0.79 ms | 0 - 27 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 0.354 ms | 0 - 27 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.682 ms | 0 - 28 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.225 ms | 0 - 24 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 0.354 ms | 0 - 27 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.305 ms | 0 - 27 MB | NPU
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.79 ms | 0 - 27 MB | NPU
| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.518 ms | 0 - 46 MB | NPU
| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 1.57 ms | 0 - 52 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 0.915 ms | 0 - 9 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.797 ms | 0 - 1 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 1.116 ms | 0 - 31 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® SA8650P | 1.116 ms | 0 - 31 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® SA8255P | 1.116 ms | 0 - 31 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® QCS8450 | 1.57 ms | 0 - 52 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 0.98 ms | 0 - 8 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 0.382 ms | 0 - 29 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® SA7255P | 2.325 ms | 0 - 28 MB | NPU
| MNASNet05 | TFLITE | float | Qualcomm® SA8295P | 1.481 ms | 0 - 28 MB | NPU
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Mobile | 0.382 ms | 0 - 29 MB | NPU
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.295 ms | 0 - 31 MB | NPU

## License
* The license for the original implementation of MNASNet05 can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py)

## Community
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).