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README.md

@@ -31,7 +31,7 @@ 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 | 15.561 ms | 6 - 13 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 11.083 ms | 6 - 30 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
 
 
 ## Installation
@@ -89,16 +89,6 @@ device. This script does the following:
 python -m qai_hub_models.models.litehrnet.export
 ```
 
-```
-Profile Job summary of LiteHRNet
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 15.63 ms
-Estimated Peak Memory Range: 6.23-10.27 MB
-Compute Units: NPU (1226),CPU (10) | Total (1236)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/LiteHRNet/export.py)