v0.57.3

See https://github.com/qualcomm/ai-hub-models/releases/v0.57.3 for changelog.

README.md

@@ -16,7 +16,7 @@ pipeline_tag: image-classification
 SwinV2Base is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
 
 This is based on the implementation of SwinV2-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
-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/v0.57.2/src/qai_hub_models/models/swinv2_base) 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/v0.57.3/src/qai_hub_models/models/swinv2_base) 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.
 
@@ -29,23 +29,23 @@ Below are pre-exported model assets ready for deployment.
 
 | Runtime | Precision | Chipset | SDK Versions | Download |
 |---|---|---|---|---|
-| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-qnn_dlc-float.zip)
-| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-qnn_dlc-w8a16.zip)
-| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-tflite-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-qnn_dlc-float.zip)
+| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-qnn_dlc-w8a16.zip)
+| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-tflite-float.zip)
 
 For more device-specific assets and performance metrics, visit **[SwinV2-Base on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/swinv2_base)**.
 
 
 ### Option 2: Export with Custom Configurations
 
-Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.2/src/qai_hub_models/models/swinv2_base) 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/v0.57.3/src/qai_hub_models/models/swinv2_base) 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 [SwinV2-Base on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.2/src/qai_hub_models/models/swinv2_base) for usage instructions.
+See our repository for [SwinV2-Base on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/swinv2_base) for usage instructions.
 
 ## Model Details
 

release_assets.json

@@ -1,5 +1,5 @@
 {
-    "version": "0.57.2",
+    "version": "0.57.3",
     "precisions": {
         "w8a16": {
             "universal_assets": {
@@ -7,7 +7,7 @@
                     "tool_versions": {
                         "qairt": "2.45.0.260326154327"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-qnn_dlc-w8a16.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-qnn_dlc-w8a16.zip"
                 }
             }
         },
@@ -18,13 +18,13 @@
                         "qairt": "2.45.0.260326154327",
                         "litert": "1.4.4"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-tflite-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-tflite-float.zip"
                 },
                 "qnn_dlc": {
                     "tool_versions": {
                         "qairt": "2.45.0.260326154327"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.2/swinv2_base-qnn_dlc-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.57.3/swinv2_base-qnn_dlc-float.zip"
                 }
             }
         }