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README.md

@@ -2,7 +2,7 @@
 datasets:
 - coco
 library_name: pytorch
-license: other
+license: apache-2.0
 pipeline_tag: image-classification
 tags:
 - android
@@ -27,15 +27,18 @@ More details on model performance across various devices, can be found
 - **Model Type:** Pose estimation
 - **Model Stats:**
   - Model checkpoint: mobilenet_v1_101
-  - Input resolution: 257x193
+  - Input resolution: 513x257
   - Number of parameters: 3.31M
   - Model size: 12.7 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 | 1.387 ms | 0 - 2 MB | FP16 | NPU |  [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.436 ms | 0 - 65 MB | FP16 | NPU |  [Posenet-Mobilenet.so](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.439 ms | 0 - 23 MB | FP16 | NPU |  [Posenet-Mobilenet.so](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.so) 
+
 
 
 ## Installation
@@ -102,9 +105,11 @@ Compute Units: NPU (69) | Total (69)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Posenet-Mobilenet/export.py)
+This [export script](https://aihub.qualcomm.com/models/posenet_mobilenet/qai_hub_models/models/Posenet-Mobilenet/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:
 
@@ -181,6 +186,7 @@ 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.
@@ -217,7 +223,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Posenet-Mobilenet can be found
   [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt).
-- 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
 * [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225)