Upload README.md with huggingface_hub

README.md

@@ -36,8 +36,8 @@ 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 | 1.921 ms | 0 - 2 MB | FP16 | NPU |  [RegNet.tflite](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.659 ms | 0 - 57 MB | FP16 | NPU |  [RegNet.so](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.974 ms | 0 - 2 MB | FP16 | NPU |  [RegNet.tflite](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.675 ms | 0 - 57 MB | FP16 | NPU |  [RegNet.so](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.so) 
 
 
 ## Installation
@@ -97,16 +97,16 @@ python -m qai_hub_models.models.regnet.export
 ```
 Profile Job summary of RegNet
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 1.92 ms
-Estimated Peak Memory Range: 0.02-1.84 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 1.36 ms
+Estimated Peak Memory Range: 0.02-125.82 MB
 Compute Units: NPU (112) | Total (112)
 
 Profile Job summary of RegNet
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 1.66 ms
-Estimated Peak Memory Range: 0.23-56.74 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 1.20 ms
+Estimated Peak Memory Range: 0.59-65.35 MB
 Compute Units: NPU (187) | Total (187)
 
 
@@ -226,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of RegNet 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
 * [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)