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README.md

@@ -37,8 +37,8 @@ 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 | 1.103 ms | 0 - 2 MB | INT8 | NPU |  [ResNet101Quantized.tflite](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.097 ms | 0 - 188 MB | INT8 | NPU |  [ResNet101Quantized.so](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.171 ms | 0 - 2 MB | INT8 | NPU |  [ResNet101Quantized.tflite](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.394 ms | 0 - 178 MB | INT8 | NPU |  [ResNet101Quantized.so](https://huggingface.co/qualcomm/ResNet101Quantized/blob/main/ResNet101Quantized.so) 
 
 
 ## Installation
@@ -95,23 +95,6 @@ device. This script does the following:
 python -m qai_hub_models.models.resnet101_quantized.export
 ```
 
-```
-Profile Job summary of ResNet101Quantized
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.86 ms
-Estimated Peak Memory Range: 0.02-87.90 MB
-Compute Units: NPU (146) | Total (146)
-
-Profile Job summary of ResNet101Quantized
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.82 ms
-Estimated Peak Memory Range: 0.16-51.42 MB
-Compute Units: NPU (143) | Total (143)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ResNet101Quantized/export.py)