---
library_name: pytorch
license: other
tags:
- bu_auto
- android
pipeline_tag: image-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/web-assets/model_demo.png)

# NASNet: Optimized for Qualcomm Devices

NASNet is a vision transformer model that can classify images from the Imagenet dataset.

This is based on the implementation of NASNet found [here](https://github.com/huggingface/pytorch-image-models/tree/main).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nasnet) 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.com) 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 |
|---|---|---|---|---|
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.46.0/nasnet-tflite-float.zip)

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


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nasnet) 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 [NASNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nasnet) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: nasnetalarge.tf_in1k
- Input resolution: 224x224
- GMACs: 5.9
- Activations (M): 19.4
- Number of parameters: 88.7M
- Model size (float): 338 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| NASNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 5.589 ms | 0 - 671 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 36.767 ms | 0 - 526 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 7.663 ms | 0 - 3 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS9075 | 10.262 ms | 0 - 191 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 23.477 ms | 0 - 648 MB | NPU
| NASNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.596 ms | 0 - 524 MB | NPU
| NASNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.11 ms | 0 - 529 MB | NPU

## License
* The license for the original implementation of NASNet can be found
  [here](https://github.com/huggingface/pytorch-image-models?tab=Apache-2.0-1-ov-file).

## References
* [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)
* [Source Model Implementation](https://github.com/huggingface/pytorch-image-models/tree/main)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).