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README.md

@@ -14,11 +14,9 @@ tags:
 
 OpenPose is a machine learning model that estimates body and hand pose in an image and returns location and confidence for each of 19 joints.
 
-This model is an implementation of OpenPose found [here](https://github.com/CMU-Perceptual-Computing-Lab/openpose).
-This repository provides scripts to run OpenPose on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/openpose).
-
+This is based on the implementation of OpenPose found
+[here]({source_repo}). More details on model performance
+accross various devices, can be found [here](https://aihub.qualcomm.com/models/openpose).
 
 ### Model Details
 
@@ -29,189 +27,66 @@ More details on model performance across various devices, can be found
   - Number of parameters: 52.3M
   - Model size: 200 MB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| OpenPose | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 11.959 ms | 0 - 2 MB | FP16 | NPU | -- |
+| OpenPose | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 11.864 ms | 1 - 205 MB | FP16 | NPU | -- |
+| OpenPose | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 12.08 ms | 0 - 114 MB | FP16 | NPU | -- |
+| OpenPose | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 11.393 ms | 0 - 41 MB | FP16 | NPU | -- |
+| OpenPose | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 11.479 ms | 1 - 18 MB | FP16 | NPU | -- |
+| OpenPose | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 11.482 ms | 0 - 45 MB | FP16 | NPU | -- |
+| OpenPose | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 11.74 ms | 0 - 3 MB | FP16 | NPU | -- |
+| OpenPose | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 12.078 ms | 1 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 11.737 ms | 0 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8255 (Proxy) | SA8255P Proxy | QNN | 12.134 ms | 1 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 11.702 ms | 0 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8775 (Proxy) | SA8775P Proxy | QNN | 12.105 ms | 1 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 11.688 ms | 0 - 2 MB | FP16 | NPU | -- |
+| OpenPose | SA8650 (Proxy) | SA8650P Proxy | QNN | 12.118 ms | 1 - 2 MB | FP16 | NPU | -- |
+| OpenPose | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 23.527 ms | 0 - 41 MB | FP16 | NPU | -- |
+| OpenPose | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 23.749 ms | 1 - 18 MB | FP16 | NPU | -- |
+| OpenPose | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 8.656 ms | 0 - 23 MB | FP16 | NPU | -- |
+| OpenPose | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 8.742 ms | 1 - 16 MB | FP16 | NPU | -- |
+| OpenPose | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 7.177 ms | 1 - 27 MB | FP16 | NPU | -- |
+| OpenPose | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 12.659 ms | 1 - 1 MB | FP16 | NPU | -- |
+| OpenPose | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 12.628 ms | 102 - 102 MB | FP16 | NPU | -- |
 
 
 
-| 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 | 11.699 ms | 0 - 2 MB | FP16 | NPU |  [OpenPose.tflite](https://huggingface.co/qualcomm/OpenPose/blob/main/OpenPose.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 11.933 ms | 1 - 219 MB | FP16 | NPU | OpenPose 
-
-
-
-## Installation
-
-This model can be installed as a Python package via pip.
-
-```bash
-pip install "qai-hub-models[openpose]"
-```
-
-
-
-## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
-
-Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
-Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
-
-With this API token, you can configure your client to run models on the cloud
-hosted devices.
-```bash
-qai-hub configure --api_token API_TOKEN
-```
-Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
-
-
-
-## Demo off target
-
-The package contains a simple end-to-end demo that downloads pre-trained
-weights and runs this model on a sample input.
-
-```bash
-python -m qai_hub_models.models.openpose.demo
-```
-
-The above demo runs a reference implementation of pre-processing, model
-inference, and post processing.
-
-**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
-environment, please add the following to your cell (instead of the above).
-```
-%run -m qai_hub_models.models.openpose.demo
-```
-
-
-### Run model on a cloud-hosted device
-
-In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
-device. This script does the following:
-* Performance check on-device on a cloud-hosted device
-* Downloads compiled assets that can be deployed on-device for Android.
-* Accuracy check between PyTorch and on-device outputs.
-
-```bash
-python -m qai_hub_models.models.openpose.export
-```
-
-```
-Profile Job summary of OpenPose
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 12.32 ms
-Estimated Peak Memory Range: 0.57-0.57 MB
-Compute Units: NPU (186) | Total (186)
-
-
-```
-
-
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/openpose/qai_hub_models/models/OpenPose/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:
-
-Step 1: **Compile model for on-device deployment**
-
-To compile a PyTorch model for on-device deployment, we first trace the model
-in memory using the `jit.trace` and then call the `submit_compile_job` API.
-
-```python
-import torch
-
-import qai_hub as hub
-from qai_hub_models.models.openpose import 
-
-# Load the model
-
-# Device
-device = hub.Device("Samsung Galaxy S23")
-
-
-```
-
-
-Step 2: **Performance profiling on cloud-hosted device**
-
-After compiling models from step 1. Models can be profiled model on-device using the
-`target_model`. Note that this scripts runs the model on a device automatically
-provisioned in the cloud.  Once the job is submitted, you can navigate to a
-provided job URL to view a variety of on-device performance metrics.
-```python
-profile_job = hub.submit_profile_job(
-    model=target_model,
-    device=device,
-)
-        
-```
-
-Step 3: **Verify on-device accuracy**
-
-To verify the accuracy of the model on-device, you can run on-device inference
-on sample input data on the same cloud hosted device.
-```python
-input_data = torch_model.sample_inputs()
-inference_job = hub.submit_inference_job(
-    model=target_model,
-    device=device,
-    inputs=input_data,
-)
-    on_device_output = inference_job.download_output_data()
-
-```
-With the output of the model, you can compute like PSNR, relative errors or
-spot check the output with expected output.
-
-**Note**: This on-device profiling and inference requires access to Qualcomm®
-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.
-
-```bash
-python -m qai_hub_models.models.openpose.demo --on-device
-```
-
-**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
-environment, please add the following to your cell (instead of the above).
-```
-%run -m qai_hub_models.models.openpose.demo -- --on-device
-```
-
-
-## Deploying compiled model to Android
-
-
-The models can be deployed using multiple runtimes:
-- TensorFlow Lite (`.tflite` export): [This
-  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
-  guide to deploy the .tflite model in an Android application.
-
-
-- QNN (`.so` export ): This [sample
-  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
-provides instructions on how to use the `.so` shared library  in an Android application.
 
+## License
+* The license for the original implementation of OpenPose can be found [here](https://cmu.flintbox.com/technologies/b820c21d-8443-4aa2-a49f-8919d93a8740).
+* The license for the compiled assets for on-device deployment can be found [here](https://cmu.flintbox.com/technologies/b820c21d-8443-4aa2-a49f-8919d93a8740)
 
-## View on Qualcomm® AI Hub
-Get more details on OpenPose's performance across various devices [here](https://aihub.qualcomm.com/models/openpose).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
-## License
-- The license for the original implementation of OpenPose can be found
-  [here](https://cmu.flintbox.com/technologies/b820c21d-8443-4aa2-a49f-8919d93a8740).
-- The license for the compiled assets for on-device deployment can be found [here](https://cmu.flintbox.com/technologies/b820c21d-8443-4aa2-a49f-8919d93a8740)
 
 ## References
 * [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1812.08008)
 * [Source Model Implementation](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
 
+
+
 ## Community
-* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).
 
-
+## Usage and Limitations
+
+Model may not be used for or in connection with any of the following applications:
+
+- Accessing essential private and public services and benefits;
+- Administration of justice and democratic processes;
+- Assessing or recognizing the emotional state of a person;
+- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
+- Education and vocational training;
+- Employment and workers management;
+- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
+- General purpose social scoring;
+- Law enforcement;
+- Management and operation of critical infrastructure;
+- Migration, asylum and border control management;
+- Predictive policing;
+- Real-time remote biometric identification in public spaces;
+- Recommender systems of social media platforms;
+- Scraping of facial images (from the internet or otherwise); and/or
+- Subliminal manipulation