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README.md

@@ -36,8 +36,8 @@ 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 | 1.895 ms | 0 - 43 MB | FP16 | NPU |  [RegNet.tflite](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.662 ms | 1 - 58 MB | FP16 | NPU |  [RegNet.so](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.314 ms | 0 - 2 MB | FP16 | NPU |  [RegNet.tflite](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.128 ms | 0 - 15 MB | FP16 | NPU |  [RegNet.so](https://huggingface.co/qualcomm/RegNet/blob/main/RegNet.so) 
 
 
 ## Installation
@@ -94,23 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.regnet.export
 ```
 
-```
-Profile Job summary of RegNet
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 1.35 ms
-Estimated Peak Memory Range: 0.01-128.45 MB
-Compute Units: NPU (112) | Total (112)
-
-Profile Job summary of RegNet
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 1.19 ms
-Estimated Peak Memory Range: 0.59-68.55 MB
-Compute Units: NPU (186) | Total (186)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/RegNet/export.py)