--- 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 CNN-based architecture discovered via Neural Architecture Search (NAS) 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/v0.57.1/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 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.57.1/nasnet-onnx-float.zip) | ONNX | w8a8_mixed_fp16 | Universal | QAIRT 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.57.1/nasnet-onnx-w8a8_mixed_fp16.zip) | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.57.1/nasnet-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nasnet/releases/v0.57.1/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/v0.57.1/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/v0.57.1/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® X2 Elite | 9.628 ms | 179 - 179 MB | NPU | NASNet | ONNX | float | Snapdragon® X Elite | 18.994 ms | 188 - 188 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 14.224 ms | 1 - 560 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 52.539 ms | 2 - 509 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 18.865 ms | 0 - 194 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS8450 | 52.539 ms | 2 - 509 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Elite Mobile | 11.763 ms | 1 - 403 MB | NPU | NASNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.431 ms | 1 - 411 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS9075 | 29.458 ms | 1 - 46 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS8750 | 11.763 ms | 1 - 403 MB | NPU | NASNet | ONNX | float | Qualcomm® QCS7181 | 18.994 ms | 188 - 188 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® X2 Elite | 4.622 ms | 180 - 180 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® X Elite | 9.438 ms | 149 - 149 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 6.51 ms | 0 - 609 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Gen 1 Mobile | 19.186 ms | 1 - 593 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | 9.226 ms | 1 - 162 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS8450 | 19.186 ms | 1 - 593 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS9075 | 10.017 ms | 0 - 46 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Elite Mobile | 5.311 ms | 1 - 487 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 4.713 ms | 0 - 478 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS8750 | 5.311 ms | 1 - 487 MB | NPU | NASNet | ONNX | w8a8_mixed_fp16 | Qualcomm® QCS7181 | 9.438 ms | 149 - 149 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® X2 Elite | 8.842 ms | 1 - 1 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® X Elite | 16.703 ms | 1 - 1 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.088 ms | 0 - 515 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 48.588 ms | 0 - 472 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS8275 | 89.226 ms | 1 - 351 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 16.242 ms | 1 - 4 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS8450 | 48.588 ms | 0 - 472 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 9.757 ms | 1 - 351 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA8295P | 38.656 ms | 1 - 310 MB | NPU | NASNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.732 ms | 0 - 367 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® SA7255P | 89.226 ms | 1 - 351 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS9075 | 26.592 ms | 3 - 6 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS8750 | 9.757 ms | 1 - 351 MB | NPU | NASNet | QNN_DLC | float | Qualcomm® QCS7181 | 16.703 ms | 1 - 1 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 12.677 ms | 0 - 695 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 50.114 ms | 0 - 652 MB | NPU | NASNet | TFLITE | float | Qualcomm® QCS8275 | 89.665 ms | 0 - 538 MB | NPU | NASNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 16.786 ms | 0 - 197 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8775P | 128.657 ms | 0 - 33 MB | GPU | NASNet | TFLITE | float | Qualcomm® SA8650P | 128.657 ms | 0 - 33 MB | GPU | NASNet | TFLITE | float | Qualcomm® SA8255P | 128.657 ms | 0 - 33 MB | GPU | NASNet | TFLITE | float | Qualcomm® QCS8450 | 50.114 ms | 0 - 652 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 10.319 ms | 0 - 533 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA8295P | 40.092 ms | 0 - 486 MB | NPU | NASNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.164 ms | 0 - 551 MB | NPU | NASNet | TFLITE | float | Qualcomm® SA7255P | 89.665 ms | 0 - 538 MB | NPU | NASNet | TFLITE | float | Qualcomm® QCS9075 | 25.195 ms | 0 - 192 MB | NPU | NASNet | TFLITE | float | Qualcomm® QCS8750 | 10.319 ms | 0 - 533 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).