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 | 0.297 ms | 0 - 1 MB | INT8 | NPU |  [GoogLeNetQuantized.tflite](https://huggingface.co/qualcomm/GoogLeNetQuantized/blob/main/GoogLeNetQuantized.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.346 ms | 0 - 133 MB | INT8 | NPU |  [GoogLeNetQuantized.so](https://huggingface.co/qualcomm/GoogLeNetQuantized/blob/main/GoogLeNetQuantized.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.297 ms | 0 - 2 MB | INT8 | NPU |  [GoogLeNetQuantized.tflite](https://huggingface.co/qualcomm/GoogLeNetQuantized/blob/main/GoogLeNetQuantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.345 ms | 0 - 4 MB | INT8 | NPU |  [GoogLeNetQuantized.so](https://huggingface.co/qualcomm/GoogLeNetQuantized/blob/main/GoogLeNetQuantized.so) 
 
 
 ## Installation
@@ -97,18 +97,11 @@ python -m qai_hub_models.models.googlenet_quantized.export
 ```
 Profile Job summary of GoogLeNetQuantized
 --------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 0.32 ms
-Estimated Peak Memory Range: 0.01-1.95 MB
+Device: Snapdragon X Elite CRD (11)
+Estimated Inference Time: 0.47 ms
+Estimated Peak Memory Range: 0.52-0.52 MB
 Compute Units: NPU (86) | Total (86)
 
-Profile Job summary of GoogLeNetQuantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 0.36 ms
-Estimated Peak Memory Range: 0.61-5.14 MB
-Compute Units: NPU (88) | Total (88)
-
 
 ```
 ## How does this work?