Upload README.md with huggingface_hub

README.md

@@ -31,10 +31,13 @@ More details on model performance across various devices, can be found
   - Model size: 17.9 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 | 6.721 ms | 0 - 3 MB | FP16 | NPU |  [Yolo-v6.tflite](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 5.377 ms | 4 - 15 MB | FP16 | NPU |  [Yolo-v6.so](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 7.424 ms | 0 - 3 MB | FP16 | NPU |  [Yolo-v6.tflite](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 5.369 ms | 5 - 16 MB | FP16 | NPU |  [Yolo-v6.so](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.so) 
+
 
 
 ## Installation
@@ -95,15 +98,17 @@ python -m qai_hub_models.models.yolov6.export
 Profile Job summary of Yolo-v6
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 6.76 ms
+Estimated Inference Time: 6.81 ms
 Estimated Peak Memory Range: 4.70-4.70 MB
 Compute Units: NPU (228) | Total (228)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Yolo-v6/export.py)
+This [export script](https://aihub.qualcomm.com/models/yolov6/qai_hub_models/models/Yolo-v6/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:
 
@@ -180,6 +185,7 @@ 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.
@@ -216,7 +222,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Yolo-v6 can be found
   [here](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/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://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE)
 
 ## References
 * [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976)