Upload README.md with huggingface_hub

README.md

@@ -34,10 +34,13 @@ More details on model performance across various devices, can be found
   - Model size: 20.2 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 | 1.618 ms | 0 - 2 MB | FP16 | NPU |  [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.684 ms | 1 - 85 MB | FP16 | NPU |  [EfficientNet-B0.so](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.626 ms | 0 - 2 MB | FP16 | NPU |  [EfficientNet-B0.tflite](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.678 ms | 0 - 301 MB | FP16 | NPU |  [EfficientNet-B0.so](https://huggingface.co/qualcomm/EfficientNet-B0/blob/main/EfficientNet-B0.so) 
+
 
 
 ## Installation
@@ -98,15 +101,17 @@ python -m qai_hub_models.models.efficientnet_b0.export
 Profile Job summary of EfficientNet-B0
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.83 ms
-Estimated Peak Memory Range: 0.57-0.57 MB
+Estimated Inference Time: 1.84 ms
+Estimated Peak Memory Range: 1.25-1.25 MB
 Compute Units: NPU (243) | Total (243)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/EfficientNet-B0/export.py)
+This [export script](https://aihub.qualcomm.com/models/efficientnet_b0/qai_hub_models/models/EfficientNet-B0/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:
 
@@ -183,6 +188,7 @@ 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.
@@ -219,7 +225,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of EfficientNet-B0 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
 * [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)