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README.md
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@@ -36,29 +36,31 @@ More details on model performance across various devices, can be found
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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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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.
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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.
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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.
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| PPE-Detection | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.
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| PPE-Detection | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.
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Device : Samsung Galaxy S23 (13)
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Runtime : TFLITE
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Estimated inference time (ms) : 0.7
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Estimated peak memory usage (MB): [0,
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Total # Ops : 77
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Compute Unit(s) : NPU (77 ops)
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```
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import torch
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import qai_hub as hub
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from qai_hub_models.models.gear_guard_net import
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# Load the model
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# Device
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device = hub.Device("Samsung Galaxy S23")
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```
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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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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.682 ms | 0 - 79 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.751 ms | 1 - 60 MB | FP16 | NPU | [PPE-Detection.so](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.so) |
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| PPE-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.053 ms | 0 - 15 MB | FP16 | NPU | [PPE-Detection.onnx](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.onnx) |
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.494 ms | 0 - 15 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.544 ms | 0 - 17 MB | FP16 | NPU | [PPE-Detection.so](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.so) |
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| PPE-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.848 ms | 0 - 46 MB | FP16 | NPU | [PPE-Detection.onnx](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.onnx) |
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.412 ms | 0 - 14 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.53 ms | 0 - 13 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.849 ms | 0 - 24 MB | FP16 | NPU | [PPE-Detection.onnx](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.onnx) |
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| PPE-Detection | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.683 ms | 0 - 70 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.728 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | SA7255P ADP | SA7255P | TFLITE | 29.048 ms | 0 - 13 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | SA7255P ADP | SA7255P | QNN | 29.306 ms | 1 - 6 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.684 ms | 0 - 80 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.738 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | SA8295P ADP | SA8295P | TFLITE | 1.822 ms | 0 - 10 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | SA8295P ADP | SA8295P | QNN | 1.991 ms | 1 - 6 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.681 ms | 0 - 6 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.743 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | SA8775P ADP | SA8775P | TFLITE | 1.567 ms | 0 - 14 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | SA8775P ADP | SA8775P | QNN | 1.787 ms | 1 - 6 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.406 ms | 0 - 14 MB | FP16 | NPU | [PPE-Detection.tflite](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.tflite) |
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| PPE-Detection | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.494 ms | 1 - 17 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.829 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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| PPE-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.142 ms | 13 - 13 MB | FP16 | NPU | [PPE-Detection.onnx](https://huggingface.co/qualcomm/PPE-Detection/blob/main/PPE-Detection.onnx) |
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Device : Samsung Galaxy S23 (13)
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Runtime : TFLITE
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Estimated inference time (ms) : 0.7
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Estimated peak memory usage (MB): [0, 79]
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Total # Ops : 77
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Compute Unit(s) : NPU (77 ops)
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```
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import torch
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import qai_hub as hub
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from qai_hub_models.models.gear_guard_net 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 S23")
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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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