Upload README.md with huggingface_hub

README.md

@@ -30,10 +30,13 @@ More details on model performance across various devices, can be found
   - Model size: 430 KB
 
 
+
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 | 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 | 1.838 ms | 0 - 2 MB | FP16 | NPU |  [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.175 ms | 0 - 7 MB | FP16 | NPU |  [SINet.so](https://huggingface.co/qualcomm/SINet/blob/main/SINet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.808 ms | 0 - 2 MB | FP16 | NPU |  [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.17 ms | 0 - 4 MB | FP16 | NPU |  [SINet.so](https://huggingface.co/qualcomm/SINet/blob/main/SINet.so) 
+
 
 
 ## Installation
@@ -94,15 +97,17 @@ python -m qai_hub_models.models.sinet.export
 Profile Job summary of SINet
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.41 ms
+Estimated Inference Time: 1.35 ms
 Estimated Peak Memory Range: 0.57-0.57 MB
 Compute Units: NPU (186) | Total (186)
 
 
 ```
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 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/SINet/export.py)
+This [export script](https://aihub.qualcomm.com/models/sinet/qai_hub_models/models/SINet/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:
 
@@ -179,6 +184,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
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 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -215,7 +221,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of SINet can be found
   [here](https://github.com/clovaai/ext_portrait_segmentation/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://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder](https://arxiv.org/abs/1911.09099)