Community curates object detection dataset list for YOLO

Dataset discovery is one of the most quietly painful parts of training object detectors, and community-curated lists like this punch above their weight for practitioners who just want to get started.

• –The list balances general-purpose benchmarks (COCO, Objects365 with 365 categories, LVIS with 1,000+) against domain-specific sets for autonomous driving (BDD100K), UAVs (UAVDT, DroneVehicle), and even agriculture (MinneApple)
• –YOLOv13, which the author is testing, is a real and recent model using hypergraph-enhanced adaptive visual perception — not just version inflation
• –The DroneVehicle RGB-Infrared dataset and UAVDT entry are genuinely underexposed picks that don't appear in standard Ultralytics documentation
• –Deepfake Detection Challenge Dataset is an odd inclusion for an object detection list — it's video classification, not bounding boxes — suggesting the curation is a work in progress
• –Score of 1 with no comments at time of scrape; value here is reference utility, not virality