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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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- backbone |
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- android |
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pipeline_tag: video-classification |
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--- |
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# ResNet-Mixed-Convolution: Optimized for Mobile Deployment |
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## Sports and human action recognition in videos |
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ResNet Mixed Convolutions is a network with a mixture of 2D and 3D convolutions used for video understanding. |
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This model is an implementation of ResNet-Mixed-Convolution found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py). |
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This repository provides scripts to run ResNet-Mixed-Convolution on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/resnet_mixed). |
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### Model Details |
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- **Model Type:** Model_use_case.video_classification |
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- **Model Stats:** |
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- Model checkpoint: Kinectics-400 |
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- Input resolution: 112x112 |
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- Number of parameters: 11.7M |
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- Model size (float): 44.7 MB |
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- Model size (w8a8): 11.7 MB |
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| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| ResNet-Mixed-Convolution | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 337.464 ms | 31 - 81 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 98.496 ms | 2 - 72 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 145.438 ms | 31 - 70 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 27.864 ms | 1 - 57 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 135.337 ms | 14 - 78 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 14.006 ms | 2 - 25 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 150.633 ms | 31 - 81 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 25.775 ms | 0 - 75 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 337.464 ms | 31 - 81 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 98.496 ms | 2 - 72 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 142.205 ms | 31 - 74 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 14.098 ms | 2 - 27 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 163.17 ms | 31 - 62 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 27.623 ms | 0 - 51 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 136.674 ms | 31 - 41 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 14.062 ms | 2 - 26 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 150.633 ms | 31 - 81 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 25.775 ms | 0 - 75 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 139.769 ms | 31 - 92 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 14.085 ms | 2 - 28 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 13.648 ms | 0 - 71 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.onnx) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 103.846 ms | 31 - 89 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 10.025 ms | 0 - 85 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 10.412 ms | 2 - 90 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.onnx) | |
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| ResNet-Mixed-Convolution | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 126.007 ms | 30 - 79 MB | NPU | [ResNet-Mixed-Convolution.tflite](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.tflite) | |
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| ResNet-Mixed-Convolution | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 10.72 ms | 2 - 79 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 10.023 ms | 2 - 83 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.onnx) | |
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| ResNet-Mixed-Convolution | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 16.103 ms | 291 - 291 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.dlc) | |
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| ResNet-Mixed-Convolution | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 15.045 ms | 22 - 22 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution.onnx) | |
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| ResNet-Mixed-Convolution | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 28.01 ms | 1 - 48 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 12.751 ms | 1 - 67 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 8.355 ms | 1 - 19 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 8.892 ms | 1 - 52 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 54.711 ms | 0 - 53 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 28.01 ms | 1 - 48 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 8.383 ms | 1 - 18 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 14.741 ms | 1 - 50 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 8.341 ms | 1 - 18 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 8.892 ms | 1 - 52 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 8.37 ms | 1 - 17 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 7.78 ms | 0 - 31 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.onnx) | |
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| ResNet-Mixed-Convolution | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 6.263 ms | 1 - 63 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 5.904 ms | 0 - 62 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.onnx) | |
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| ResNet-Mixed-Convolution | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 5.839 ms | 1 - 48 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 5.708 ms | 1 - 48 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.onnx) | |
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| ResNet-Mixed-Convolution | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 9.986 ms | 29 - 29 MB | NPU | [ResNet-Mixed-Convolution.dlc](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.dlc) | |
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| ResNet-Mixed-Convolution | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.298 ms | 11 - 11 MB | NPU | [ResNet-Mixed-Convolution.onnx](https://huggingface.co/qualcomm/ResNet-Mixed-Convolution/blob/main/ResNet-Mixed-Convolution_w8a16.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[resnet-mixed]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.resnet_mixed.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.resnet_mixed.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.resnet_mixed.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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ResNet-Mixed-Convolution |
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Device : cs_8275 (ANDROID 14) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 337.5 |
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Estimated peak memory usage (MB): [31, 81] |
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Total # Ops : 57 |
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Compute Unit(s) : npu (53 ops) gpu (0 ops) cpu (4 ops) |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/resnet_mixed/qai_hub_models/models/ResNet-Mixed-Convolution/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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on-device. Lets go through each step below in detail: |
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Step 1: **Compile model for on-device deployment** |
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.resnet_mixed import Model |
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# Load the model |
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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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sample_inputs = torch_model.sample_inputs() |
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()]) |
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# Compile model on a specific device |
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compile_job = hub.submit_compile_job( |
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model=pt_model, |
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device=device, |
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input_specs=torch_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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target_model = compile_job.get_target_model() |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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After compiling models from step 1. Models can be profiled model on-device using the |
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`target_model`. Note that this scripts runs the model on a device automatically |
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provisioned in the cloud. Once the job is submitted, you can navigate to a |
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provided job URL to view a variety of on-device performance metrics. |
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```python |
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profile_job = hub.submit_profile_job( |
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model=target_model, |
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device=device, |
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) |
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``` |
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Step 3: **Verify on-device accuracy** |
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To verify the accuracy of the model on-device, you can run on-device inference |
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on sample input data on the same cloud hosted device. |
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```python |
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input_data = torch_model.sample_inputs() |
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inference_job = hub.submit_inference_job( |
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model=target_model, |
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device=device, |
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inputs=input_data, |
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) |
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on_device_output = inference_job.download_output_data() |
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``` |
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With the output of the model, you can compute like PSNR, relative errors or |
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spot check the output with expected output. |
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**Note**: This on-device profiling and inference requires access to Qualcomm® |
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AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN (`.so` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on ResNet-Mixed-Convolution's performance across various devices [here](https://aihub.qualcomm.com/models/resnet_mixed). |
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) |
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## License |
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* The license for the original implementation of ResNet-Mixed-Convolution 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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## References |
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* [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248) |
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* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py) |
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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:[email protected]). |
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