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README.md

@@ -34,7 +34,7 @@ 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 | 439.506 ms | 8 - 17 MB | FP16 | NPU |  [DETR-ResNet101-DC5.tflite](https://huggingface.co/qualcomm/DETR-ResNet101-DC5/blob/main/DETR-ResNet101-DC5.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 407.929 ms | 7 - 15 MB | FP16 | NPU |  [DETR-ResNet101-DC5.tflite](https://huggingface.co/qualcomm/DETR-ResNet101-DC5/blob/main/DETR-ResNet101-DC5.tflite) 
 
 
 ## Installation
@@ -92,16 +92,6 @@ device. This script does the following:
 python -m qai_hub_models.models.detr_resnet101_dc5.export
 ```
 
-```
-Profile Job summary of DETR-ResNet101-DC5
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 331.40 ms
-Estimated Peak Memory Range: 0.10-435.99 MB
-Compute Units: NPU (955),GPU (2),CPU (1) | Total (958)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/DETR-ResNet101-DC5/export.py)