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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 | 1.218 ms | 0 - 2 MB | FP16 | NPU |  [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.223 ms | 1 - 7 MB | FP16 | NPU |  [EfficientNet-B0.so](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.637 ms | 0 - 17 MB | FP16 | NPU |  [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.692 ms | 0 - 85 MB | FP16 | NPU |  [EfficientNet-B0.so](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.so) 
 
 
 ## Installation
@@ -94,23 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.efficientnet_b0.export
 ```
 
-```
-Profile Job summary of EfficientNet-B0
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.91 ms
-Estimated Peak Memory Range: 0.01-67.19 MB
-Compute Units: NPU (243) | Total (243)
-
-Profile Job summary of EfficientNet-B0
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.89 ms
-Estimated Peak Memory Range: 0.00-67.70 MB
-Compute Units: NPU (241) | Total (241)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/EfficientNet-B0/export.py)