v0.66.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.66.0 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/qualcomm/ai-hub-models/blob/v0.65.0/src/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/v0.66.0/src/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 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-onnx-float.zip)
-| QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-qnn_dlc-float.zip)
-| TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-tflite-float.zip)
+| ONNX | float | Universal | QAIRT 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/litehrnet-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/litehrnet-qnn_dlc-float.zip)
+| TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/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/qualcomm/ai-hub-models/blob/v0.65.0/src/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/v0.66.0/src/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/qualcomm/ai-hub-models/blob/v0.65.0/src/qai_hub_models/models/litehrnet) for usage instructions.
+See our repository for [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.66.0/src/qai_hub_models/models/litehrnet) for usage instructions.
 
 ## Model Details
 

release_assets.json

@@ -1,5 +1,5 @@
 {
-    "version": "0.65.0",
+    "version": "0.66.0",
     "precisions": {
         "float": {
             "universal_assets": {
@@ -8,19 +8,19 @@
                         "qairt": "2.50.0.260828221209",
                         "onnx_runtime": "1.30.0"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-onnx-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/litehrnet-onnx-float.zip"
                 },
                 "qnn_dlc": {
                     "tool_versions": {
                         "qairt": "2.50.0.260828221209"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-qnn_dlc-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/litehrnet-qnn_dlc-float.zip"
                 },
                 "tflite": {
                     "tool_versions": {
                         "qairt": "2.50.0.260828221209"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.65.0/litehrnet-tflite-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.66.0/litehrnet-tflite-float.zip"
                 }
             }
         }