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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 | 73.806 ms | 3 - 6 MB | FP16 | NPU |  [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 69.637 ms | 0 - 103 MB | FP16 | NPU |  [ESRGAN.so](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 65.051 ms | 3 - 7 MB | FP16 | NPU |  [ESRGAN.tflite](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 65.381 ms | 0 - 100 MB | FP16 | NPU |  [ESRGAN.so](https://huggingface.co/qualcomm/ESRGAN/blob/main/ESRGAN.so) 
 
 
 ## Installation
@@ -90,23 +90,6 @@ device. This script does the following:
 python -m qai_hub_models.models.esrgan.export
 ```
 
-```
-Profile Job summary of ESRGAN
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 50.71 ms
-Estimated Peak Memory Range: 0.07-555.32 MB
-Compute Units: NPU (1024) | Total (1024)
-
-Profile Job summary of ESRGAN
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 49.72 ms
-Estimated Peak Memory Range: 1.25-244.22 MB
-Compute Units: NPU (1026) | Total (1026)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ESRGAN/export.py)