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library_name: pytorch
license: other
tags:
- bu_auto
- android
pipeline_tag: image-classification
---
# 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/qualcomm/ai-hub-models/blob/main/src/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 |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.50.2/nasnet-onnx-float.zip)
| ONNX | w8a8_mixed_fp16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.50.2/nasnet-onnx-w8a8_mixed_fp16.zip)
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.50.2/nasnet-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.50.2/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/qualcomm/ai-hub-models/blob/main/src/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/qualcomm/ai-hub-models/blob/main/src/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 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.39 ms | 1 - 673 MB | NPU
| NASNet | ONNX | float | Snapdragon® X2 Elite | 10.699 ms | 189 - 189 MB | NPU
| NASNet | ONNX | float | Snapdragon® X Elite | 17.795 ms | 188 - 188 MB | NPU
| NASNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 12.781 ms | 1 - 838 MB | NPU
| NASNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 17.495 ms | 0 - 197 MB | NPU
| NASNet | ONNX | float | Qualcomm® QCS9075 | 28.261 ms | 0 - 4 MB | NPU
| NASNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 10.374 ms | 0 - 640 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 4.516 ms | 5 - 359 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® X2 Elite | 4.488 ms | 100 - 100 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® X Elite | 12.004 ms | 98 - 98 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 6.901 ms | 6 - 478 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | 9.747 ms | 5 - 10 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS9075 | 11.55 ms | 5 - 8 MB | NPU
| NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.535 ms | 5 - 361 MB | NPU
| NASNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.769 ms | 0 - 664 MB | NPU
| NASNet | QNN_DLC | float | Snapdragon® X2 Elite | 10.154 ms | 1 - 1 MB | NPU
| NASNet | QNN_DLC | float | Snapdragon® X Elite | 19.289 ms | 1 - 1 MB | NPU
| NASNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.32 ms | 0 - 813 MB | NPU
| NASNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 54.013 ms | 1 - 660 MB | NPU
| NASNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 19.02 ms | 1 - 3 MB | NPU
| NASNet | QNN_DLC | float | Qualcomm® QCS9075 | 28.662 ms | 1 - 3 MB | NPU
| NASNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 35.42 ms | 0 - 793 MB | NPU
| NASNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.17 ms | 0 - 650 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.843 ms | 0 - 379 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Snapdragon® X2 Elite | 4.111 ms | 0 - 0 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Snapdragon® X Elite | 9.146 ms | 0 - 0 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 6.018 ms | 0 - 493 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Qualcomm® QCS8275 (Proxy) | 16.35 ms | 0 - 379 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | 8.843 ms | 0 - 2 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Qualcomm® QCS9075 | 9.426 ms | 0 - 2 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Qualcomm® QCS8450 (Proxy) | 10.984 ms | 0 - 505 MB | NPU
| NASNet | QNN_DLC | w8a8_mixed_fp16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.001 ms | 0 - 378 MB | NPU
| NASNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.624 ms | 2 - 618 MB | NPU
| NASNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 8.785 ms | 0 - 778 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 44.298 ms | 0 - 629 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 12.486 ms | 0 - 3 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS9075 | 15.553 ms | 0 - 192 MB | NPU
| NASNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 28.988 ms | 0 - 758 MB | NPU
| NASNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.952 ms | 0 - 633 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).
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