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README.md

@@ -36,7 +36,7 @@ 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 | 6.651 ms | 1 - 3 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 6.617 ms | 0 - 2 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
 
 
 ## Installation
@@ -93,16 +93,6 @@ device. This script does the following:
 python -m qai_hub_models.models.ddrnet23_slim.export
 ```
 
-```
-Profile Job summary of DDRNet23-Slim
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 6.68 ms
-Estimated Peak Memory Range: 0.96-2.92 MB
-Compute Units: NPU (131) | Total (131)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/DDRNet23-Slim/export.py)