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README.md
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 2.
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 2.
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 2.
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.
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| ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 2.
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| ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 2.
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| ResNet50 | SA7255P ADP | SA7255P | TFLITE | 76.
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| ResNet50 | SA7255P ADP | SA7255P | QNN | 77.
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| ResNet50 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 2.
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| ResNet50 | SA8255 (Proxy) | SA8255P Proxy | QNN | 2.
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| ResNet50 | SA8295P ADP | SA8295P | TFLITE | 3.
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| ResNet50 | SA8295P ADP | SA8295P | QNN | 4.
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| ResNet50 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 2.
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| ResNet50 | SA8650 (Proxy) | SA8650P Proxy | QNN | 2.
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| ResNet50 | SA8775P ADP | SA8775P | TFLITE | 4.
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| ResNet50 | SA8775P ADP | SA8775P | QNN | 4.
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| ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 3.
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| ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 3.
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| ResNet50 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.
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| ResNet50 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.
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## Installation
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This model can be installed as a Python package via pip.
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```bash
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pip install qai-hub-models
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```
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy
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# Trace model
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input_shape = torch_model.get_input_spec()
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## License
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* The license for the original implementation of ResNet50 can be found
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* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 2.269 ms | 0 - 244 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 2.383 ms | 0 - 173 MB | FP16 | NPU | [ResNet50.so](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.so) |
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| ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 2.298 ms | 0 - 238 MB | FP16 | NPU | [ResNet50.onnx](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.onnx) |
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.615 ms | 0 - 29 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.689 ms | 1 - 30 MB | FP16 | NPU | [ResNet50.so](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.so) |
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| ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.727 ms | 0 - 33 MB | FP16 | NPU | [ResNet50.onnx](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.onnx) |
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.346 ms | 0 - 27 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.623 ms | 1 - 28 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.669 ms | 1 - 28 MB | FP16 | NPU | [ResNet50.onnx](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.onnx) |
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| ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 2.273 ms | 0 - 243 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 2.186 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | SA7255P ADP | SA7255P | TFLITE | 76.179 ms | 0 - 23 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | SA7255P ADP | SA7255P | QNN | 77.365 ms | 1 - 9 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 2.274 ms | 0 - 244 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | SA8255 (Proxy) | SA8255P Proxy | QNN | 2.188 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | SA8295P ADP | SA8295P | TFLITE | 3.777 ms | 0 - 21 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | SA8295P ADP | SA8295P | QNN | 4.04 ms | 0 - 14 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 2.273 ms | 0 - 243 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | SA8650 (Proxy) | SA8650P Proxy | QNN | 2.179 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | SA8775P ADP | SA8775P | TFLITE | 4.149 ms | 0 - 23 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | SA8775P ADP | SA8775P | QNN | 4.215 ms | 0 - 10 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 3.096 ms | 0 - 24 MB | FP16 | NPU | [ResNet50.tflite](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite) |
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| ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 3.137 ms | 1 - 20 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.328 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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| ResNet50 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.334 ms | 51 - 51 MB | FP16 | NPU | [ResNet50.onnx](https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.onnx) |
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## Installation
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Install the package via pip:
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```bash
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pip install qai-hub-models
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```
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S24")
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# Trace model
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input_shape = torch_model.get_input_spec()
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## License
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* The license for the original implementation of ResNet50 can be found
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[here](https://github.com/pytorch/vision/blob/main/LICENSE).
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* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
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