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README.md

@@ -35,7 +35,7 @@ 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 | 15.912 ms | 0 - 23 MB | FP16 | NPU |  [Yolo-v7.tflite](https://huggingface.co/qualcomm/Yolo-v7/blob/main/Yolo-v7.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 24.96 ms | 36 - 68 MB | FP16 | GPU |  [Yolo-v7.tflite](https://huggingface.co/qualcomm/Yolo-v7/blob/main/Yolo-v7.tflite) 
 
 
 
@@ -94,6 +94,16 @@ device. This script does the following:
 python -m qai_hub_models.models.yolov7.export
 ```
 
+```
+Profile Job summary of Yolo-v7
+--------------------------------------------------
+Device: SA8775 (Proxy) (13)
+Estimated Inference Time: 24.59 ms
+Estimated Peak Memory Range: 38.27-87.46 MB
+Compute Units: GPU (145),CPU (70) | Total (215)
+
+
+```
 
 
 ## How does this work?
@@ -115,7 +125,6 @@ from qai_hub_models.models.yolov7 import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")