Upload README.md with huggingface_hub

README.md

@@ -35,10 +35,13 @@ More details on model performance across various devices, can be found
   - Model size: 9.72 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 | 0.834 ms | 0 - 2 MB | FP16 | NPU |  [MobileNet-v3-Small.tflite](https://huggingface.co/qualcomm/MobileNet-v3-Small/blob/main/MobileNet-v3-Small.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.866 ms | 0 - 44 MB | FP16 | NPU |  [MobileNet-v3-Small.so](https://huggingface.co/qualcomm/MobileNet-v3-Small/blob/main/MobileNet-v3-Small.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.835 ms | 0 - 2 MB | FP16 | NPU |  [MobileNet-v3-Small.tflite](https://huggingface.co/qualcomm/MobileNet-v3-Small/blob/main/MobileNet-v3-Small.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.882 ms | 0 - 13 MB | FP16 | NPU |  [MobileNet-v3-Small.so](https://huggingface.co/qualcomm/MobileNet-v3-Small/blob/main/MobileNet-v3-Small.so) 
+
 
 
 ## Installation
@@ -99,15 +102,17 @@ python -m qai_hub_models.models.mobilenet_v3_small.export
 Profile Job summary of MobileNet-v3-Small
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.06 ms
-Estimated Peak Memory Range: 0.57-0.57 MB
+Estimated Inference Time: 1.02 ms
+Estimated Peak Memory Range: 1.19-1.19 MB
 Compute Units: NPU (126) | Total (126)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/MobileNet-v3-Small/export.py)
+This [export script](https://aihub.qualcomm.com/models/mobilenet_v3_small/qai_hub_models/models/MobileNet-v3-Small/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:
 
@@ -184,6 +189,7 @@ 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.
@@ -220,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of MobileNet-v3-Small 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
 * [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)