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: 68.8 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 | 25.448 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 19.884 ms | 24 - 47 MB | FP16 | NPU |  [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 25.403 ms | 4 - 7 MB | FP16 | NPU |  [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 20.253 ms | 24 - 47 MB | FP16 | NPU |  [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) 
+
 
 
 ## Installation
@@ -97,15 +100,17 @@ python -m qai_hub_models.models.ffnet_54s.export
 Profile Job summary of FFNet-54S
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 25.83 ms
+Estimated Inference Time: 25.73 ms
 Estimated Peak Memory Range: 24.05-24.05 MB
 Compute Units: NPU (175) | Total (175)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/FFNet-54S/export.py)
+This [export script](https://aihub.qualcomm.com/models/ffnet_54s/qai_hub_models/models/FFNet-54S/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 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 
+
 ## Deploying compiled model to Android
 
 
@@ -204,7 +210,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FFNet-54S can be found
   [here](https://github.com/Qualcomm-AI-research/FFNet/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
 * [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236)