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README.md

@@ -33,7 +33,7 @@ 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 | 11.261 ms | 6 - 13 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 11.191 ms | 6 - 28 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
 
 
 
@@ -95,9 +95,9 @@ python -m qai_hub_models.models.litehrnet.export
 ```
 Profile Job summary of LiteHRNet
 --------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 11.18 ms
-Estimated Peak Memory Range: 6.26-17.18 MB
+Device: SA8775 (Proxy) (13)
+Estimated Inference Time: 11.20 ms
+Estimated Peak Memory Range: 6.23-189.47 MB
 Compute Units: NPU (1226),CPU (10) | Total (1236)
 
 
@@ -123,7 +123,6 @@ from qai_hub_models.models.litehrnet import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")