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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 | 1.054 ms | 0 - 2 MB | FP16 | NPU |  [ResNet18.tflite](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.98 ms | 0 - 80 MB | FP16 | NPU |  [ResNet18.so](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.053 ms | 0 - 2 MB | FP16 | NPU |  [ResNet18.tflite](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.989 ms | 0 - 81 MB | FP16 | NPU |  [ResNet18.so](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.so) 
 
 
 ## Installation
@@ -97,16 +97,16 @@ python -m qai_hub_models.models.resnet18.export
 ```
 Profile Job summary of ResNet18
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 1.05 ms
-Estimated Peak Memory Range: 0.01-1.64 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.77 ms
+Estimated Peak Memory Range: 0.01-22.53 MB
 Compute Units: NPU (36) | Total (36)
 
 Profile Job summary of ResNet18
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.98 ms
-Estimated Peak Memory Range: 0.02-80.45 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.72 ms
+Estimated Peak Memory Range: 0.60-24.10 MB
 Compute Units: NPU (52) | Total (52)
 
 
@@ -226,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of ResNet18 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
 * [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)