DIOR is a large-scale benchmark dataset for object detection in optical remote sensing (aerial/satellite) images. It contains approximately 23,463 images (800×800 px) and ~192,472 axis-aligned object instances covering 20 object categories (e.g., airplane, airport, ship, bridge, stadium, vehicle, windmill, storage tank, dam, chimney, golf course, tennis court, baseball field, basketball court, expressway toll station/service area, harbor, overpass, ground track field, train station). Images have varying spatial resolutions (~0.5 m to 30 m). Standard splits are provided (training, validation, test — commonly reported splits: train ~5,862, val ~5,863, test ~11,725). DIOR is typically evaluated using object-detection metrics such as mean Average Precision (mAP). A rotated-box variant (DIOR-R) with oriented bounding-box annotations has also been released/used by the community.
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