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README.md

@@ -33,8 +33,8 @@ 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 | 7.224 ms | 0 - 8 MB | FP16 | NPU |  [Yolo-v6.tflite](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 6.898 ms | 5 - 18 MB | FP16 | NPU |  [Yolo-v6.so](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 7.953 ms | 2 - 5 MB | FP16 | NPU |  [Yolo-v6.tflite](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 6.885 ms | 5 - 18 MB | FP16 | NPU |  [Yolo-v6.so](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.so) 
 
 
 ## Installation
@@ -91,23 +91,6 @@ device. This script does the following:
 python -m qai_hub_models.models.yolov6.export
 ```
 
-```
-Profile Job summary of Yolo-v6
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 5.15 ms
-Estimated Peak Memory Range: 0.04-78.21 MB
-Compute Units: NPU (182) | Total (182)
-
-Profile Job summary of Yolo-v6
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 4.87 ms
-Estimated Peak Memory Range: 4.72-89.10 MB
-Compute Units: NPU (229) | Total (229)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Yolo-v6/export.py)