Upload README.md with huggingface_hub

README.md

@@ -32,8 +32,8 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 8.563 ms | 4 - 11 MB | FP16 | NPU |  [FCN_ResNet50.tflite](https://huggingface.co/qualcomm/FCN_ResNet50/blob/main/FCN_ResNet50.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 7.864 ms | 0 - 12 MB | FP16 | NPU |  [FCN_ResNet50.so](https://huggingface.co/qualcomm/FCN_ResNet50/blob/main/FCN_ResNet50.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 8.55 ms | 4 - 6 MB | FP16 | NPU |  [FCN_ResNet50.tflite](https://huggingface.co/qualcomm/FCN_ResNet50/blob/main/FCN_ResNet50.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 7.881 ms | 0 - 13 MB | FP16 | NPU |  [FCN_ResNet50.so](https://huggingface.co/qualcomm/FCN_ResNet50/blob/main/FCN_ResNet50.so) 
 
 
 ## Installation
@@ -93,16 +93,16 @@ python -m qai_hub_models.models.fcn_resnet50.export
 ```
 Profile Job summary of FCN_ResNet50
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 8.56 ms
-Estimated Peak Memory Range: 4.07-10.54 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 6.41 ms
+Estimated Peak Memory Range: 4.05-72.84 MB
 Compute Units: NPU (84) | Total (84)
 
 Profile Job summary of FCN_ResNet50
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 7.86 ms
-Estimated Peak Memory Range: 0.02-12.48 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 5.85 ms
+Estimated Peak Memory Range: 0.61-53.34 MB
 Compute Units: NPU (126) | Total (126)
 
 
@@ -222,7 +222,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FCN_ResNet50 can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
-- 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).
+- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
 
 ## References
 * [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1411.4038)