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@@ -36,30 +36,29 @@ 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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- | EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 22320.423 ms | 24 - 129 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
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- | EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1646.642 ms | 4 - 417 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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- | EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16466.939 ms | 24 - 469 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
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- | EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1941.249 ms | 133 - 777 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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- | EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 15526.488 ms | 0 - 493 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1417.374 ms | 103 - 811 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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- | EfficientViT-l2-seg | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 15479.41 ms | 26 - 29 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-l2-seg | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 21636.69 ms | 24 - 271 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 16472.568 ms | 24 - 24 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 3680.622 ms | 145 - 145 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.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[efficientvit_l2_seg]"
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  ```
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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
@@ -110,8 +109,8 @@ Profiling Results
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  EfficientViT-l2-seg
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  Device : Samsung Galaxy S23 (13)
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  Runtime : QNN
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- Estimated inference time (ms) : 22320.4
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- Estimated peak memory usage (MB): [24, 129]
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  Total # Ops : 775
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  Compute Unit(s) : NPU (775 ops)
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  ```
@@ -138,7 +137,7 @@ from qai_hub_models.models.efficientvit_l2_seg 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()
@@ -230,7 +229,8 @@ 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 EfficientViT-l2-seg can be found [here](https://github.com/CVHub520/efficientvit/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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+ | EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 22178.805 ms | 24 - 124 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
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+ | EfficientViT-l2-seg | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1921.957 ms | 5 - 354 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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+ | EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16480.291 ms | 11 - 455 MB | FP16 | NPU | [EfficientViT-l2-seg.so](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.so) |
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+ | EfficientViT-l2-seg | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1995.319 ms | 132 - 775 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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+ | EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 19442.548 ms | 24 - 516 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-l2-seg | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1445.969 ms | 101 - 809 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.onnx) |
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+ | EfficientViT-l2-seg | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 15540.11 ms | 26 - 30 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-l2-seg | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 21630.243 ms | 16 - 262 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 16477.289 ms | 24 - 24 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-l2-seg | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2870.496 ms | 144 - 144 MB | FP16 | NPU | [EfficientViT-l2-seg.onnx](https://huggingface.co/qualcomm/EfficientViT-l2-seg/blob/main/EfficientViT-l2-seg.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[efficientvit-l2-seg]"
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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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  EfficientViT-l2-seg
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  Device : Samsung Galaxy S23 (13)
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  Runtime : QNN
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+ Estimated inference time (ms) : 22178.8
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+ Estimated peak memory usage (MB): [24, 124]
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  Total # Ops : 775
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  Compute Unit(s) : NPU (775 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 EfficientViT-l2-seg can be found
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+ [here](https://github.com/CVHub520/efficientvit/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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