Upload README.md with huggingface_hub

README.md

@@ -32,10 +32,13 @@ More details on model performance across various devices, can be found
   - Model size: 118 MB
 
 
+
+
 | 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 | 155.816 ms | 6 - 218 MB | FP16 | NPU |  [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 150.601 ms | 10 - 31 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 159.228 ms | 6 - 106 MB | FP16 | NPU |  [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 156.519 ms | 9 - 30 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
+
 
 
 ## Installation
@@ -96,15 +99,17 @@ python -m qai_hub_models.models.unet_segmentation.export
 Profile Job summary of Unet-Segmentation
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 190.38 ms
-Estimated Peak Memory Range: 9.39-9.39 MB
+Estimated Inference Time: 190.48 ms
+Estimated Peak Memory Range: 9.40-9.40 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/Unet-Segmentation/export.py)
+This [export script](https://aihub.qualcomm.com/models/unet_segmentation/qai_hub_models/models/Unet-Segmentation/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -181,6 +186,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
+
 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -217,7 +223,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Unet-Segmentation can be found
   [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- The license for the compiled assets for on-device deployment can be found [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
 
 ## References
 * [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)