Upload README.md with huggingface_hub

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 | 1.038 ms | 0 - 2 MB | FP16 | NPU |  [ResNet18.tflite](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.985 ms | 0 - 91 MB | FP16 | NPU |  [ResNet18.so](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.398 ms | 0 - 2 MB | FP16 | NPU |  [ResNet18.tflite](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.489 ms | 0 - 80 MB | FP16 | NPU |  [ResNet18.so](https://huggingface.co/qualcomm/ResNet18/blob/main/ResNet18.so) 
 
 
 ## Installation
@@ -94,23 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.resnet18.export
 ```
 
-```
-Profile Job summary of ResNet18
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.77 ms
-Estimated Peak Memory Range: 0.01-22.55 MB
-Compute Units: NPU (36) | Total (36)
-
-Profile Job summary of ResNet18
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.72 ms
-Estimated Peak Memory Range: 0.59-25.44 MB
-Compute Units: NPU (51) | Total (51)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ResNet18/export.py)