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README.md

@@ -34,7 +34,7 @@ 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 | 292.98 ms | 2 - 143 MB | FP16 | GPU |  [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 294.244 ms | 0 - 142 MB | FP16 | GPU |  [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) 
 
 
 
@@ -95,9 +95,9 @@ python -m qai_hub_models.models.deeplabv3_resnet50.export
 ```
 Profile Job summary of DeepLabV3-ResNet50
 --------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 291.24 ms
-Estimated Peak Memory Range: 5.22-174.24 MB
+Device: SA8775 (Proxy) (13)
+Estimated Inference Time: 291.81 ms
+Estimated Peak Memory Range: 2.10-142.41 MB
 Compute Units: GPU (95) | Total (95)
 
 
@@ -123,7 +123,6 @@ from qai_hub_models.models.deeplabv3_resnet50 import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")