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README.md

@@ -15,7 +15,7 @@ tags:
 
 HRNet performs pose estimation in high-resolution representations.
 
-This model is an implementation of HRNetPoseQuantized found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/hrnet_posenet).
+This model is an implementation of HRNetPoseQuantized found [here]({source_repo}).
 This repository provides scripts to run HRNetPoseQuantized on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/hrnet_pose_quantized).
@@ -30,15 +30,31 @@ More details on model performance across various devices, can be found
  - Number of parameters: 28.5M
  - Model size: 109 MB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| HRNetPoseQuantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.956 ms | 0 - 2 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.251 ms | 0 - 8 MB | INT8 | NPU | [HRNetPoseQuantized.so](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.so) |
+| HRNetPoseQuantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.792 ms | 0 - 109 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.051 ms | 0 - 33 MB | INT8 | NPU | [HRNetPoseQuantized.so](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.so) |
+| HRNetPoseQuantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 3.825 ms | 0 - 69 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 5.523 ms | 0 - 8 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 17.117 ms | 0 - 4 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.953 ms | 0 - 3 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.199 ms | 0 - 1 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.952 ms | 0 - 2 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.211 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.962 ms | 0 - 2 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.221 ms | 0 - 1 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.96 ms | 0 - 18 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.212 ms | 0 - 1 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.172 ms | 0 - 109 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.524 ms | 0 - 38 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.663 ms | 0 - 64 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) |
+| HRNetPoseQuantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.746 ms | 0 - 32 MB | INT8 | NPU | Use Export Script |
+| HRNetPoseQuantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.343 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |
 
 
 
-| 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 | 0.966 ms | 0 - 2 MB | INT8 | NPU | [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.25 ms | 0 - 21 MB | INT8 | NPU | [HRNetPoseQuantized.so](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.so) 
-
-
 
 ## Installation
 
@@ -94,16 +110,16 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.hrnet_pose_quantized.export
 ```
-
 ```
-Profile Job summary of HRNetPoseQuantized
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1.33 ms
-Estimated Peak Memory Range: 0.31-0.31 MB
-Compute Units: NPU (487) | Total (487)
-
-
+Profiling Results
+------------------------------------------------------------
+HRNetPoseQuantized
+Device : Samsung Galaxy S23 (13)
+Runtime : TFLITE 
+Estimated inference time (ms) : 1.0 
+Estimated peak memory usage (MB): [0, 2] 
+Total # Ops : 518 
+Compute Unit(s) : NPU (518 ops) 
 ```
 
 
@@ -142,15 +158,19 @@ provides instructions on how to use the `.so` shared library in an Android appl
 Get more details on HRNetPoseQuantized's performance across various devices [here](https://aihub.qualcomm.com/models/hrnet_pose_quantized).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
+
 ## License
-- The license for the original implementation of HRNetPoseQuantized can be found
- [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
-- 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)
+* The license for the original implementation of HRNetPoseQuantized can be found [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
+* 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
 * [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212)
 * [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/hrnet_posenet)
 
+
+
 ## Community
 * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).