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README.md

@@ -14,7 +14,7 @@ tags:
 
 LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
 
-This model is an implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
+This model is an implementation of LiteHRNet found [here]({source_repo}).
 This repository provides scripts to run LiteHRNet on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/litehrnet).
@@ -28,14 +28,23 @@ More details on model performance across various devices, can be found
   - Number of parameters: 1.11M
   - Model size: 4.56 MB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| LiteHRNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.959 ms | 0 - 3 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.13 ms | 0 - 7 MB | FP16 | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
+| LiteHRNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.91 ms | 0 - 95 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.533 ms | 1 - 107 MB | FP16 | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
+| LiteHRNet | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.938 ms | 0 - 2 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 7.965 ms | 0 - 3 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 7.929 ms | 0 - 2 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 7.934 ms | 0 - 3 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 8.522 ms | 0 - 84 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.295 ms | 0 - 68 MB | FP16 | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
+| LiteHRNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.83 ms | 1 - 80 MB | FP16 | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
+| LiteHRNet | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.063 ms | 4 - 4 MB | FP16 | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
 
 
 
-| 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 | 7.904 ms | 0 - 4 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
-
-
 
 ## Installation
 
@@ -91,16 +100,16 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.litehrnet.export
 ```
-
 ```
-Profile Job summary of LiteHRNet
---------------------------------------------------
-Device: SA8255 (Proxy) (13)
-Estimated Inference Time: 7.90 ms
-Estimated Peak Memory Range: 0.25-2.38 MB
-Compute Units: NPU (1233),CPU (2) | Total (1235)
-
-
+Profiling Results
+------------------------------------------------------------
+LiteHRNet
+Device                          : Samsung Galaxy S23 (13)   
+Runtime                         : TFLITE                    
+Estimated inference time (ms)   : 8.0                       
+Estimated peak memory usage (MB): [0, 3]                    
+Total # Ops                     : 1235                      
+Compute Unit(s)                 : NPU (1233 ops) CPU (2 ops)
 ```
 
 
@@ -199,15 +208,19 @@ provides instructions on how to use the `.so` shared library  in an Android appl
 Get more details on LiteHRNet's performance across various devices [here](https://aihub.qualcomm.com/models/litehrnet).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
+
 ## License
-- The license for the original implementation of LiteHRNet can be found
-  [here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/LICENSE).
-- 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 LiteHRNet can be found [here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/LICENSE).
+* 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
 * [Lite-HRNet: A Lightweight High-Resolution Network](https://arxiv.org/abs/2104.06403)
 * [Source Model Implementation](https://github.com/HRNet/Lite-HRNet)
 
+
+
 ## 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]).