Upload README.md with huggingface_hub

README.md

@@ -33,10 +33,13 @@ More details on model performance across various devices, can be found
   - Model size: 5.25 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 | 1.228 ms | 0 - 2 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.779 ms | 0 - 18 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.229 ms | 0 - 2 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.767 ms | 0 - 122 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
+
 
 
 ## Installation
@@ -97,15 +100,17 @@ python -m qai_hub_models.models.shufflenet_v2.export
 Profile Job summary of Shufflenet-v2
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 0.95 ms
+Estimated Inference Time: 1.09 ms
 Estimated Peak Memory Range: 0.57-0.57 MB
 Compute Units: NPU (158) | Total (158)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Shufflenet-v2/export.py)
+This [export script](https://aihub.qualcomm.com/models/shufflenet_v2/qai_hub_models/models/Shufflenet-v2/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:
 
@@ -182,6 +187,7 @@ 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.
@@ -218,7 +224,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Shufflenet-v2 can be found
   [here](https://github.com/pytorch/vision/blob/main/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
 * [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)