Overview Initial release of the inspector-roofing-roof-damage-yolo model. This YOLO (You Only Look Once) based computer vision model is trained to automatically detect and classify common signs of roof damage from drone and high-resolution aerial imagery. Key Features & Capabilities Damage Classification: Detects missing shingles, wind damage, and hail impacts on various roofing materials. High-Speed Inference: Optimized for rapid batch-processing of property inspection images. Integration-Ready: Designed to integrate seamlessly with the Inspector Roofing / InstantRoofView ecosystem for automated damage reports. Model Performance (v1.0.0) Architecture: [Insert YOLO version, e.g., YOLOv8x] mAP50: [Insert Accuracy metric, e.g., 0.85] Dataset: Trained on a custom dataset of [Insert Number] annotated residential roof images. Installation & Quick Start To run inference using this model, clone the repository and install the requirements: git clone [https://github.com/RichNass87/inspector-roofing-roof-damage-yolo.git](https://github.com/RichNass87/inspector-roofing-roof-damage-yolo.git) cd inspector-roofing-roof-damage-yolo pip install -r requirements.txt