NASNet / README.md

v0.50.2

ee0d54f verified 7 days ago

7.42 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/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).

	---
	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/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).