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@@ -37,7 +37,8 @@ More details on model performance across various devices, can be found
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  | 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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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 74.926 ms | 0 - 3 MB | FP16 | NPU | [ResNet101Quantized.tflite](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.tflite)
 
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  ## Installation
@@ -97,10 +98,17 @@ python -m qai_hub_models.models.resnet101_quantized.export
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  ```
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  Profile Job summary of ResNet101Quantized
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  --------------------------------------------------
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- Device: Samsung Galaxy S23 Ultra (13)
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- Estimated Inference Time: 74.93 ms
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- Estimated Peak Memory Range: 0.14-2.63 MB
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- Compute Units: NPU (149) | Total (149)
 
 
 
 
 
 
 
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  ```
@@ -219,7 +227,7 @@ 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 ResNet101Quantized 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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  * [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
 
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  | 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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+ | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.122 ms | 0 - 2 MB | INT8 | NPU | [ResNet101Quantized.tflite](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.tflite)
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+ | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.101 ms | 0 - 188 MB | INT8 | NPU | [ResNet101Quantized.so](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.so)
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  ## Installation
 
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  ```
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  Profile Job summary of ResNet101Quantized
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  --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.84 ms
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+ Estimated Peak Memory Range: 0.01-87.01 MB
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+ Compute Units: NPU (146) | Total (146)
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+
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+ Profile Job summary of ResNet101Quantized
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+ --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.83 ms
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+ Estimated Peak Memory Range: 0.16-51.47 MB
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+ Compute Units: NPU (144) | Total (144)
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  ```
 
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  ## License
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  - The license for the original implementation of ResNet101Quantized 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]({deploy_license_url})
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  ## References
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  * [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)