{"name": "Vector To YOLO", "package_name": "vector_to_yolo", "description": "This plugin generates image chips for YOLO training", "about": "VecToYOLO: Geospatial Training Data Generator\r\n\r\nVecToYOLO is a powerful QGIS plugin designed to streamline the creation of deep learning datasets. It automates the process of converting large-scale satellite imagery or drone orthomosaics into localized image \"chips\" (patches) with corresponding label files, formatted specifically for the YOLO (You Only Look Once) object detection and segmentation framework.\r\n\r\nCore Features\r\n\r\nDual Task Support: Supports both Object Detection (bounding boxes) and Instance Segmentation (polygon-to-mask conversion).\r\n\r\nFlexible Data Handling: * 8-bit Drone Orthomosaics: Standard RGB processing.\r\n\r\n16-bit Satellite Imagery: Automatic reflectance scaling (e.g., Sentinel-2 L2A) to 8-bit PNG for model compatibility.\r\n\r\nAdvanced Data Splitting: Choose between Random splitting or Spatial splitting (using geographic rows) to ensure more robust validation and prevent data leakage.\r\n\r\nCustomizable Overlap: Control the overlap percentage between chips\u2014crucial for small object detection or continuous landcover mapping.\r\n\r\nReady-to-Train Output: Generates a structured directory with train/ and val/ folders, a classes.txt file, and a pre-configured data.yaml file tailored for Kaggle, Google Colab, or Local environments.\r\n\r\nHow it Works\r\n\r\nAlignment: Automatically reprojects your vector annotations to match the CRS of your raster.\r\n\r\nTiling: Iterates through your specified map extent, creating image tiles based on your desired chip size and overlap.\r\n\r\nLabel Mapping: Maps vector attributes to class IDs and converts geographic coordinates into the normalized YOLO format.", "homepage": "https://hicsuntdracone.github.io/vector-to-yolo/", "repository": "https://github.com/hicsuntdracone/vector-to-yolo", "tracker": "https://github.com/hicsuntdracone/vector-to-yolo/issues", "author": "Yogi Satrio Wijoyo", "tags": ["vector", "raster", "labeling", "machine learning", "computer vision", "semantic segmentation", "yolo"], "downloads": 181, "latest_version": "0.1", "versions": [{"version": "0.1", "experimental": true, "qgis_min": "3.0.0", "qgis_max": "3.99.0", "downloads": 181, "uploaded_by": "hicsuntdracone", "upload_datetime": "2026-03-26T10:43:35.863209"}]}