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README.md

@@ -25,7 +25,7 @@ More details on model performance across various devices, can be found
 - **Model Type:** Super resolution
 - **Model Stats:**
   - Model checkpoint: quicksrnet_small_3x_checkpoint
-  - Input resolution: 640x360
+  - Input resolution: 128x128
   - Number of parameters: 27.2K
   - Model size: 110 KB
 
@@ -34,8 +34,8 @@ 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 | 1.334 ms | 0 - 2 MB | FP16 | NPU |  [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.004 ms | 0 - 10 MB | FP16 | NPU |  [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.328 ms | 0 - 4 MB | FP16 | NPU |  [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.017 ms | 0 - 3 MB | FP16 | NPU |  [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) 
 
 
 
@@ -124,7 +124,6 @@ from qai_hub_models.models.quicksrnetsmall import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")