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--- |
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library_name: pytorch |
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license: other |
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tags: |
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- bu_auto |
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- android |
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pipeline_tag: image-classification |
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--- |
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# NASNet: Optimized for Qualcomm Devices |
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NASNet is a vision transformer model that can classify images from the Imagenet dataset. |
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This is based on the implementation of NASNet found [here](https://github.com/huggingface/pytorch-image-models/tree/main). |
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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/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). |
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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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## Getting Started |
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There are two ways to deploy this model on your device: |
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### Option 1: Download Pre-Exported Models |
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Below are pre-exported model assets ready for deployment. |
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| Runtime | Precision | Chipset | SDK Versions | Download | |
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| 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) |
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For more device-specific assets and performance metrics, visit **[NASNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/nasnet)**. |
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### Option 2: Export with Custom Configurations |
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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: |
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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 [NASNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nasnet) for usage instructions. |
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## Model Details |
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**Model Type:** Model_use_case.image_classification |
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**Model Stats:** |
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- Model checkpoint: nasnetalarge.tf_in1k |
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- Input resolution: 224x224 |
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- GMACs: 5.9 |
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- Activations (M): 19.4 |
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- Number of parameters: 88.7M |
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- Model size (float): 338 MB |
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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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| NASNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 5.589 ms | 0 - 671 MB | NPU |
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| NASNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 36.767 ms | 0 - 526 MB | NPU |
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| NASNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 7.663 ms | 0 - 3 MB | NPU |
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| NASNet | TFLITE | float | Qualcomm® QCS9075 | 10.262 ms | 0 - 191 MB | NPU |
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| NASNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 23.477 ms | 0 - 648 MB | NPU |
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| NASNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.596 ms | 0 - 524 MB | NPU |
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| NASNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.11 ms | 0 - 529 MB | NPU |
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## License |
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* The license for the original implementation of NASNet can be found |
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[here](https://github.com/huggingface/pytorch-image-models?tab=Apache-2.0-1-ov-file). |
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## References |
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* [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) |
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* [Source Model Implementation](https://github.com/huggingface/pytorch-image-models/tree/main) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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