qualcomm
/

YOLO-WORLD

@@ -15,18 +15,18 @@ pipeline_tag: object-detection
 Ultralytics YOLO-World is an open-vocabulary object detection model that uses a prompt-then-detect strategy to predict bounding boxes for user-specified classes.
 This is based on the implementation of YOLO-WORLD found [here](https://github.com/AILab-CVC/YOLO-World).
-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/v0.62.1/src/qai_hub_models/models/yolo_world) 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.
 ## Getting Started
 Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
-Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.62.1/src/qai_hub_models/models/yolo_world) 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
-See our repository for [YOLO-WORLD on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.62.1/src/qai_hub_models/models/yolo_world) for usage instructions.
 ## Model Details

 Ultralytics YOLO-World is an open-vocabulary object detection model that uses a prompt-then-detect strategy to predict bounding boxes for user-specified classes.
 This is based on the implementation of YOLO-WORLD found [here](https://github.com/AILab-CVC/YOLO-World).
+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/v0.62.2/src/qai_hub_models/models/yolo_world) 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.
 ## Getting Started
 Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.62.2/src/qai_hub_models/models/yolo_world) 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
+See our repository for [YOLO-WORLD on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.62.2/src/qai_hub_models/models/yolo_world) for usage instructions.
 ## Model Details