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 | 0.921 ms | 0 - 2 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.321 ms | 1 - 4 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.919 ms | 0 - 2 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.322 ms | 1 - 4 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
 
 
 ## Installation
@@ -96,16 +96,16 @@ python -m qai_hub_models.models.shufflenet_v2.export
 ```
 Profile Job summary of Shufflenet-v2
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.92 ms
-Estimated Peak Memory Range: 0.02-2.22 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.59 ms
+Estimated Peak Memory Range: 0.02-31.31 MB
 Compute Units: NPU (202) | Total (202)
 
 Profile Job summary of Shufflenet-v2
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.32 ms
-Estimated Peak Memory Range: 0.59-3.99 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.23 ms
+Estimated Peak Memory Range: 0.01-46.20 MB
 Compute Units: NPU (157) | Total (157)
 
 
@@ -225,7 +225,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Shufflenet-v2 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
 * [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)