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@@ -35,29 +35,29 @@ 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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  |---|---|---|---|---|---|---|---|---|
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- | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 3.609 ms | 0 - 281 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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- | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 3.708 ms | 0 - 240 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) |
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- | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 3.561 ms | 0 - 202 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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- | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 2.65 ms | 0 - 32 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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- | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 2.701 ms | 1 - 28 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) |
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- | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 2.628 ms | 0 - 38 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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- | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 2.49 ms | 0 - 30 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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- | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.591 ms | 0 - 29 MB | FP16 | NPU | Use Export Script |
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- | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 2.542 ms | 0 - 33 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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- | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 3.606 ms | 0 - 281 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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- | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.334 ms | 1 - 4 MB | FP16 | NPU | Use Export Script |
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- | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 7.313 ms | 0 - 40 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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- | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 7.31 ms | 1 - 36 MB | FP16 | NPU | Use Export Script |
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- | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.996 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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- | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 3.707 ms | 47 - 47 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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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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  ```
@@ -114,7 +114,7 @@ EfficientNet-B4
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 3.6
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- Estimated peak memory usage (MB): [0, 281]
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  Total # Ops : 482
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  Compute Unit(s) : NPU (482 ops)
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  ```
@@ -141,7 +141,7 @@ from qai_hub_models.models.efficientnet_b4 import 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()
@@ -233,7 +233,8 @@ 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 EfficientNet-B4 can be found [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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  | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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+ | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 3.615 ms | 0 - 280 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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+ | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 3.707 ms | 0 - 240 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) |
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+ | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 3.564 ms | 0 - 201 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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+ | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 2.627 ms | 0 - 30 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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+ | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 2.688 ms | 1 - 29 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) |
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+ | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 2.604 ms | 0 - 36 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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+ | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 2.479 ms | 0 - 30 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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+ | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.55 ms | 1 - 29 MB | FP16 | NPU | Use Export Script |
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+ | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 2.557 ms | 1 - 34 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) |
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+ | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 3.602 ms | 0 - 281 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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+ | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.34 ms | 1 - 4 MB | FP16 | NPU | Use Export Script |
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+ | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 7.29 ms | 0 - 40 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) |
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+ | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 7.294 ms | 1 - 38 MB | FP16 | NPU | Use Export Script |
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+ | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.642 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 3.693 ms | 47 - 47 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.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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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 3.6
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+ Estimated peak memory usage (MB): [0, 280]
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  Total # Ops : 482
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  Compute Unit(s) : NPU (482 ops)
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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 EfficientNet-B4 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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