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README.md

@@ -24,16 +24,19 @@ More details on model performance across various devices, can be found
 
 - **Model Type:** Super resolution
 - **Model Stats:**
-  - Model checkpoint: quicksrnet_large_4x_checkpoint_float32
-  - Input resolution: 128x128
-  - Number of parameters: 436K
-  - Model size: 1.67 MB
+  - Model checkpoint: quicksrnet_large_3x_checkpoint
+  - Input resolution: 640x360
+  - Number of parameters: 424K
+  - Model size: 1.63 MB
+
+
 
 
 | 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 | 2.401 ms | 0 - 16 MB | FP16 | NPU |  [QuickSRNetLarge.tflite](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.092 ms | 0 - 12 MB | FP16 | NPU |  [QuickSRNetLarge.so](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.412 ms | 0 - 1 MB | FP16 | NPU |  [QuickSRNetLarge.tflite](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.108 ms | 0 - 5 MB | FP16 | NPU |  [QuickSRNetLarge.so](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.so) 
+
 
 
 ## Installation
@@ -95,14 +98,16 @@ Profile Job summary of QuickSRNetLarge
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
 Estimated Inference Time: 2.95 ms
-Estimated Peak Memory Range: 0.24-0.24 MB
+Estimated Peak Memory Range: 0.20-0.20 MB
 Compute Units: NPU (31) | Total (31)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/QuickSRNetLarge/export.py)
+This [export script](https://aihub.qualcomm.com/models/quicksrnetlarge/qai_hub_models/models/QuickSRNetLarge/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -179,6 +184,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
+
 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -215,7 +221,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of QuickSRNetLarge can be found
   [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- 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)
 
 ## References
 * [QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms](https://arxiv.org/abs/2303.04336)