Upload README.md with huggingface_hub

README.md

@@ -32,14 +32,17 @@ More details on model performance across various devices, can be found
   - Model size (MediaPipeHandLandmarkDetector): 7.71 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 | 0.955 ms | 0 - 2 MB | FP16 | NPU |  [MediaPipeHandDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.205 ms | 0 - 1 MB | FP16 | NPU |  [MediaPipeHandLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.012 ms | 0 - 10 MB | FP16 | NPU |  [MediaPipeHandDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.26 ms | 0 - 11 MB | FP16 | NPU |  [MediaPipeHandDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.205 ms | 0 - 2 MB | FP16 | NPU |  [MediaPipeHandLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.017 ms | 0 - 21 MB | FP16 | NPU |  [MediaPipeHandDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.so) 
 | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.299 ms | 1 - 9 MB | FP16 | NPU |  [MediaPipeHandLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.so) 
 
 
+
 ## Installation
 
 This model can be installed as a Python package via pip.
@@ -98,22 +101,24 @@ python -m qai_hub_models.models.mediapipe_hand.export
 Profile Job summary of MediaPipeHandDetector
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.06 ms
+Estimated Inference Time: 1.04 ms
 Estimated Peak Memory Range: 0.75-0.75 MB
 Compute Units: NPU (196) | Total (196)
 
 Profile Job summary of MediaPipeHandLandmarkDetector
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.47 ms
-Estimated Peak Memory Range: 0.75-0.75 MB
+Estimated Inference Time: 1.51 ms
+Estimated Peak Memory Range: 1.10-1.10 MB
 Compute Units: NPU (209) | Total (209)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/MediaPipe-Hand-Detection/export.py)
+This [export script](https://aihub.qualcomm.com/models/mediapipe_hand/qai_hub_models/models/MediaPipe-Hand-Detection/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:
 
@@ -191,6 +196,7 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 
+
 ## Deploying compiled model to Android
 
 
@@ -212,7 +218,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of MediaPipe-Hand-Detection can be found
   [here](https://github.com/zmurez/MediaPipePyTorch/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
 * [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)