v0.48.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.48.0 for changelog.

README.md

@@ -14,7 +14,7 @@ pipeline_tag: keypoint-detection
 LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
 
 This is based on the implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
-This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
 
 Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
 
@@ -27,23 +27,23 @@ Below are pre-exported model assets ready for deployment.
 
 | Runtime | Precision | Chipset | SDK Versions | Download |
 |---|---|---|---|---|
-| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.47.0/litehrnet-onnx-float.zip)
-| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.47.0/litehrnet-qnn_dlc-float.zip)
-| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.47.0/litehrnet-tflite-float.zip)
+| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.48.0/litehrnet-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.48.0/litehrnet-qnn_dlc-float.zip)
+| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.48.0/litehrnet-tflite-float.zip)
 
 For more device-specific assets and performance metrics, visit **[LiteHRNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/litehrnet)**.
 
 
 ### Option 2: Export with Custom Configurations
 
-Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) Python library to compile and export the model with your own:
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) Python library to compile and export the model with your own:
 - Custom weights (e.g., fine-tuned checkpoints)
 - Custom input shapes
 - Target device and runtime configurations
 
 This option is ideal if you need to customize the model beyond the default configuration provided here.
 
-See our repository for [LiteHRNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) for usage instructions.
+See our repository for [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/litehrnet) for usage instructions.
 
 ## Model Details
 
@@ -57,12 +57,13 @@ See our repository for [LiteHRNet on GitHub](https://github.com/quic/ai-hub-mode
 ## Performance Summary
 | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
 |---|---|---|---|---|---|---
+| LiteHRNet | ONNX | float | Snapdragon® X2 Elite | 2.847 ms | 5 - 5 MB | NPU
 | LiteHRNet | ONNX | float | Snapdragon® X Elite | 5.775 ms | 5 - 5 MB | NPU
 | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.131 ms | 0 - 120 MB | NPU
 | LiteHRNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 5.183 ms | 0 - 8 MB | NPU
 | LiteHRNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.846 ms | 0 - 98 MB | NPU
 | LiteHRNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.734 ms | 1 - 99 MB | NPU
-| LiteHRNet | ONNX | float | Snapdragon® X2 Elite | 2.847 ms | 5 - 5 MB | NPU
+| LiteHRNet | QNN_DLC | float | Snapdragon® X2 Elite | 1.245 ms | 1 - 1 MB | NPU
 | LiteHRNet | QNN_DLC | float | Snapdragon® X Elite | 2.388 ms | 1 - 1 MB | NPU
 | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.365 ms | 0 - 108 MB | NPU
 | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 4.906 ms | 1 - 80 MB | NPU
@@ -74,7 +75,6 @@ See our repository for [LiteHRNet on GitHub](https://github.com/quic/ai-hub-mode
 | LiteHRNet | QNN_DLC | float | Qualcomm® SA8295P | 3.472 ms | 0 - 78 MB | NPU
 | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.012 ms | 0 - 82 MB | NPU
 | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.834 ms | 1 - 84 MB | NPU
-| LiteHRNet | QNN_DLC | float | Snapdragon® X2 Elite | 1.245 ms | 1 - 1 MB | NPU
 | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.708 ms | 0 - 151 MB | NPU
 | LiteHRNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 8.745 ms | 0 - 114 MB | NPU
 | LiteHRNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4.22 ms | 0 - 2 MB | NPU