NASNet / README.md

v0.48.0

a01ac32 verified about 1 month ago

7.62 kB

	---
	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/qualcomm/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 \|
	\|---\|---\|---\|---\|---\|
	\| 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/nasnet/releases/v0.48.0/nasnet-onnx-float.zip)
	\| ONNX \| w8a8_mixed_fp16 \| Universal \| QAIRT 2.42, ONNX Runtime 1.24.1 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.48.0/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/v0.48.0/nasnet-qnn_dlc-float.zip)
	\| QNN_DLC \| w8a8_mixed_fp16 \| Universal \| QAIRT 2.43 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.48.0/nasnet-qnn_dlc-w8a8_mixed_fp16.zip)
	\| 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/nasnet/releases/v0.48.0/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/qualcomm/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/qualcomm/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 \| ONNX \| float \| Snapdragon® X2 Elite \| 8.215 ms \| 189 - 189 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® X Elite \| 17.826 ms \| 188 - 188 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 12.748 ms \| 0 - 831 MB \| NPU
	\| NASNet \| ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 17.461 ms \| 0 - 196 MB \| NPU
	\| NASNet \| ONNX \| float \| Qualcomm® QCS9075 \| 28.336 ms \| 0 - 4 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 10.601 ms \| 1 - 676 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 8.389 ms \| 1 - 673 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® X2 Elite \| 4.546 ms \| 100 - 100 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® X Elite \| 11.742 ms \| 98 - 98 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Gen 3 Mobile \| 6.989 ms \| 5 - 481 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Qualcomm® QCS8550 (Proxy) \| 9.916 ms \| 5 - 11 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Qualcomm® QCS9075 \| 11.712 ms \| 5 - 8 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 5.62 ms \| 0 - 346 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 4.568 ms \| 5 - 359 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® X2 Elite \| 9.24 ms \| 1 - 1 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® X Elite \| 19.234 ms \| 1 - 1 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Gen 3 Mobile \| 12.326 ms \| 0 - 814 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8275 (Proxy) \| 54.016 ms \| 1 - 659 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8550 (Proxy) \| 19.0 ms \| 1 - 3 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS9075 \| 28.724 ms \| 1 - 3 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8450 (Proxy) \| 35.267 ms \| 0 - 792 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 11.072 ms \| 1 - 652 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 8.904 ms \| 1 - 654 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® X2 Elite \| 4.134 ms \| 0 - 0 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® X Elite \| 9.236 ms \| 0 - 0 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Gen 3 Mobile \| 5.992 ms \| 0 - 495 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8275 (Proxy) \| 16.472 ms \| 0 - 378 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8550 (Proxy) \| 8.936 ms \| 0 - 2 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS9075 \| 9.454 ms \| 0 - 2 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8450 (Proxy) \| 11.194 ms \| 0 - 507 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.926 ms \| 0 - 377 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.895 ms \| 0 - 378 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Gen 3 Mobile \| 8.796 ms \| 0 - 783 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8275 (Proxy) \| 44.427 ms \| 0 - 628 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8550 (Proxy) \| 12.456 ms \| 0 - 3 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS9075 \| 15.469 ms \| 0 - 192 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8450 (Proxy) \| 28.855 ms \| 0 - 762 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 6.956 ms \| 0 - 623 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 5.632 ms \| 0 - 625 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).

	---
	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/qualcomm/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 \|
	\|---\|---\|---\|---\|---\|
	\| 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/nasnet/releases/v0.48.0/nasnet-onnx-float.zip)
	\| ONNX \| w8a8_mixed_fp16 \| Universal \| QAIRT 2.42, ONNX Runtime 1.24.1 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.48.0/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/v0.48.0/nasnet-qnn_dlc-float.zip)
	\| QNN_DLC \| w8a8_mixed_fp16 \| Universal \| QAIRT 2.43 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.48.0/nasnet-qnn_dlc-w8a8_mixed_fp16.zip)
	\| 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/nasnet/releases/v0.48.0/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/qualcomm/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/qualcomm/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 \| ONNX \| float \| Snapdragon® X2 Elite \| 8.215 ms \| 189 - 189 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® X Elite \| 17.826 ms \| 188 - 188 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 12.748 ms \| 0 - 831 MB \| NPU
	\| NASNet \| ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 17.461 ms \| 0 - 196 MB \| NPU
	\| NASNet \| ONNX \| float \| Qualcomm® QCS9075 \| 28.336 ms \| 0 - 4 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 10.601 ms \| 1 - 676 MB \| NPU
	\| NASNet \| ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 8.389 ms \| 1 - 673 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® X2 Elite \| 4.546 ms \| 100 - 100 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® X Elite \| 11.742 ms \| 98 - 98 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Gen 3 Mobile \| 6.989 ms \| 5 - 481 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Qualcomm® QCS8550 (Proxy) \| 9.916 ms \| 5 - 11 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Qualcomm® QCS9075 \| 11.712 ms \| 5 - 8 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 5.62 ms \| 0 - 346 MB \| NPU
	\| NASNet \| ONNX \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 4.568 ms \| 5 - 359 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® X2 Elite \| 9.24 ms \| 1 - 1 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® X Elite \| 19.234 ms \| 1 - 1 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Gen 3 Mobile \| 12.326 ms \| 0 - 814 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8275 (Proxy) \| 54.016 ms \| 1 - 659 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8550 (Proxy) \| 19.0 ms \| 1 - 3 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS9075 \| 28.724 ms \| 1 - 3 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Qualcomm® QCS8450 (Proxy) \| 35.267 ms \| 0 - 792 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 11.072 ms \| 1 - 652 MB \| NPU
	\| NASNet \| QNN_DLC \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 8.904 ms \| 1 - 654 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® X2 Elite \| 4.134 ms \| 0 - 0 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® X Elite \| 9.236 ms \| 0 - 0 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Gen 3 Mobile \| 5.992 ms \| 0 - 495 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8275 (Proxy) \| 16.472 ms \| 0 - 378 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8550 (Proxy) \| 8.936 ms \| 0 - 2 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS9075 \| 9.454 ms \| 0 - 2 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Qualcomm® QCS8450 (Proxy) \| 11.194 ms \| 0 - 507 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.926 ms \| 0 - 377 MB \| NPU
	\| NASNet \| QNN_DLC \| w8a8_mixed_fp16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.895 ms \| 0 - 378 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Gen 3 Mobile \| 8.796 ms \| 0 - 783 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8275 (Proxy) \| 44.427 ms \| 0 - 628 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8550 (Proxy) \| 12.456 ms \| 0 - 3 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS9075 \| 15.469 ms \| 0 - 192 MB \| NPU
	\| NASNet \| TFLITE \| float \| Qualcomm® QCS8450 (Proxy) \| 28.855 ms \| 0 - 762 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 6.956 ms \| 0 - 623 MB \| NPU
	\| NASNet \| TFLITE \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 5.632 ms \| 0 - 625 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).