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# RoadEye Unified Pothole Detection Dataset for YOLO

The **RoadEye** dataset aggregates annotated and augmented road-scene images from Roboflow, Mendeley, Kaggle, and custom synthetic collections for pothole detection and road-condition analysis. It provides a consistent **YOLO annotation format** across ~12 k images, split into training, validation, and test sets.

To access training scripts go here:
https://github.com/parthubhe/RoadEye_Backend

* **Domain**: Autonomous driving / road monitoring
* **Tasks**: Object detection, instance segmentation
* **Format**: YOLO (bounding box annotations)
* **Size**: ~12 k images, ~25 k annotated instances
* **License**: Combination of Roboflow, Mendeley, Kaggle, and CC-BY-4.0 (see individual source licenses)