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README.md

@@ -32,8 +32,8 @@ 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 | 58.066 ms | 0 - 164 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 | QNN Model Library | 145.873 ms | 1 - 9 MB | FP16 | GPU |  [DeepLabV3-ResNet50.so](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 58.164 ms | 0 - 164 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 | QNN Model Library | 145.136 ms | 1 - 8 MB | FP16 | GPU |  [DeepLabV3-ResNet50.so](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.so) 
 
 
 ## Installation
@@ -90,23 +90,6 @@ device. This script does the following:
 python -m qai_hub_models.models.deeplabv3_resnet50.export
 ```
 
-```
-Profile Job summary of DeepLabV3-ResNet50
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 40.35 ms
-Estimated Peak Memory Range: 0.00-26.88 MB
-Compute Units: GPU (96) | Total (96)
-
-Profile Job summary of DeepLabV3-ResNet50
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 104.95 ms
-Estimated Peak Memory Range: 0.67-25.39 MB
-Compute Units: GPU (82) | Total (82)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/DeepLabV3-ResNet50/export.py)