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README.md

@@ -38,8 +38,8 @@ 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 | 1.416 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 | 1.446 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 | 1.414 ms | 0 - 1 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 | 1.446 ms | 0 - 69 MB | FP16 | NPU |  [ResNet18.so](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.so) 
 
 
 
@@ -101,7 +101,7 @@ python -m qai_hub_models.models.resnet18.export
 Profile Job summary of ResNet18
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.57 ms
+Estimated Inference Time: 1.56 ms
 Estimated Peak Memory Range: 0.57-0.57 MB
 Compute Units: NPU (53) | Total (53)
 
@@ -128,7 +128,6 @@ from qai_hub_models.models.resnet18 import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")