Upload README.md with huggingface_hub

README.md

@@ -35,8 +35,8 @@ 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 | 1.471 ms | 0 - 2 MB | FP16 | NPU |  [GoogLeNet.tflite](https://huggingface.co/qualcomm/GoogLeNet/blob/main/GoogLeNet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.808 ms | 0 - 30 MB | FP16 | NPU |  [GoogLeNet.so](https://huggingface.co/qualcomm/GoogLeNet/blob/main/GoogLeNet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.041 ms | 0 - 2 MB | FP16 | NPU |  [GoogLeNet.tflite](https://huggingface.co/qualcomm/GoogLeNet/blob/main/GoogLeNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.083 ms | 0 - 25 MB | FP16 | NPU |  [GoogLeNet.so](https://huggingface.co/qualcomm/GoogLeNet/blob/main/GoogLeNet.so) 
 
 
 ## Installation
@@ -96,17 +96,17 @@ python -m qai_hub_models.models.googlenet.export
 ```
 Profile Job summary of GoogLeNet
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 1.47 ms
-Estimated Peak Memory Range: 0.02-1.77 MB
-Compute Units: NPU (94) | Total (94)
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.65 ms
+Estimated Peak Memory Range: 0.02-43.31 MB
+Compute Units: NPU (84) | Total (84)
 
 Profile Job summary of GoogLeNet
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 1.81 ms
-Estimated Peak Memory Range: 0.02-29.72 MB
-Compute Units: NPU (156) | Total (156)
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.68 ms
+Estimated Peak Memory Range: 0.00-47.66 MB
+Compute Units: NPU (144) | Total (144)
 
 
 ```
@@ -225,7 +225,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of GoogLeNet 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
 * [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842)