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README.md

@@ -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 | 1.808 ms | 0 - 2 MB | FP16 | NPU |  [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.17 ms | 0 - 4 MB | FP16 | NPU |  [SINet.so](https://huggingface.co/qualcomm/SINet/blob/main/SINet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.758 ms | 0 - 2 MB | FP16 | NPU |  [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.168 ms | 1 - 27 MB | FP16 | NPU |  [SINet.so](https://huggingface.co/qualcomm/SINet/blob/main/SINet.so) 
 
 
 
@@ -97,7 +97,7 @@ python -m qai_hub_models.models.sinet.export
 Profile Job summary of SINet
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.35 ms
+Estimated Inference Time: 1.32 ms
 Estimated Peak Memory Range: 0.57-0.57 MB
 Compute Units: NPU (186) | Total (186)
 
@@ -124,7 +124,6 @@ from qai_hub_models.models.sinet import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")