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README.md

@@ -18,11 +18,9 @@ tags:
 
 YoloNAS is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the COCO dataset.
 
-This model is an implementation of Yolo-NAS-Quantized found [here](https://github.com/Deci-AI/super-gradients).
-This repository provides scripts to run Yolo-NAS-Quantized on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/yolonas_quantized).
-
+This is based on the implementation of Yolo-NAS-Quantized found
+[here]({source_repo}). More details on model performance
+accross various devices, can be found [here](https://aihub.qualcomm.com/models/yolonas_quantized).
 
 ### Model Details
 
@@ -33,128 +31,54 @@ More details on model performance across various devices, can be found
   - Number of parameters: 12.2M
   - Model size: 12.1 MB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| Yolo-NAS-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 4.715 ms | 0 - 2 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 3.058 ms | 0 - 80 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 13.608 ms | 0 - 66 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 4.692 ms | 0 - 1 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 4.704 ms | 0 - 4 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 4.696 ms | 0 - 7 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 4.69 ms | 0 - 17 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 5.202 ms | 0 - 82 MB | INT8 | NPU | -- |
+| Yolo-NAS-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.157 ms | 0 - 55 MB | INT8 | 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 | 4.789 ms | 0 - 13 MB | INT8 | NPU |  [Yolo-NAS-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-NAS-Quantized/blob/main/Yolo-NAS-Quantized.tflite) 
-
-
-
-## Installation
-
-This model can be installed as a Python package via pip.
-
-```bash
-pip install "qai-hub-models[yolonas_quantized]"
-```
-
-
-
-## 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.yolonas_quantized.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.yolonas_quantized.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.yolonas_quantized.export
-```
-
-```
-Profile Job summary of Yolo-NAS-Quantized
---------------------------------------------------
-Device: RB3 Gen 2 (Proxy) (12)
-Estimated Inference Time: 13.95 ms
-Estimated Peak Memory Range: 0.07-66.05 MB
-Compute Units: NPU (204) | Total (204)
-
-
-```
-
-
-
-
-## Run demo on a cloud-hosted device
-
-You can also run the demo on-device.
-
-```bash
-python -m qai_hub_models.models.yolonas_quantized.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.yolonas_quantized.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 Yolo-NAS-Quantized can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md#license).
+* The license for the compiled assets for on-device deployment can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md)
 
-## View on Qualcomm® AI Hub
-Get more details on Yolo-NAS-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/yolonas_quantized).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
-## License
-- The license for the original implementation of Yolo-NAS-Quantized can be found
-  [here](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md#license).
-- The license for the compiled assets for on-device deployment can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md)
 
 ## References
 * [YOLO-NAS by Deci Achieves SOTA Performance on Object Detection Using Neural Architecture Search](https://deci.ai/blog/yolo-nas-object-detection-foundation-model/)
 * [Source Model Implementation](https://github.com/Deci-AI/super-gradients)
 
+
+
 ## 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:ai-hub-support@qti.qualcomm.com).
 
-
+## 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