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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 | 159.228 ms | 6 - 106 MB | FP16 | NPU |  [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 156.519 ms | 9 - 30 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 160.376 ms | 6 - 442 MB | FP16 | NPU |  [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 155.942 ms | 9 - 27 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
 
 
 
@@ -99,8 +99,8 @@ python -m qai_hub_models.models.unet_segmentation.export
 Profile Job summary of Unet-Segmentation
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 190.48 ms
-Estimated Peak Memory Range: 9.40-9.40 MB
+Estimated Inference Time: 133.37 ms
+Estimated Peak Memory Range: 9.39-9.39 MB
 Compute Units: NPU (51) | Total (51)
 
 
@@ -126,7 +126,6 @@ from qai_hub_models.models.unet_segmentation import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")