Yolov11s Drone Detector

--- license: apache-2.0 tags: - yolo - object-detection - drone-detection - uav - ultralytics library_name: ultralytics --- # YOLOv11s Drone Detector A YOLOv11s model fine-tuned to detect drones from RGB camera footage. Trained from the `pathikg/drone-detection-dataset` (54k images) and validated on the held-out **Anti-UAV-RGBT** test split (91 video sequences). ## Held-out eval (Anti-UAV-RGBT, IoU=0.5) | metric | value | |---|---| | Precision | 0.929 | | Recall | 0.759 | | F1 | 0.836 | | Mean IoU on TPs | 0.998 | | [email protected] | 0.741 | Beats every other public single-class drone YOLO we found on HF on mAP@50 and mAP@50-95 (and is much smaller — 19MB). ## Use ```python from ultralytics import YOLO model = YOLO("sapoepsilon/yolov11s-drone-detector") model.track("path/to/drone_video.mp4", tracker="bytetrack.yaml") ``` ## Training | | | |---|---| | base | ultralytics yolo11s.pt | | dataset | pathikg/drone-detection-dataset (~54k images) | | imgsz | 640 | | batch | 192 (3-GPU DDP) | | epochs | 25 (early-stopped from 30) | | optimizer | AdamW, lr 4e-4, cosine | | augmentation | mosaic + flip + close-mosaic at epoch 20 | | hardware | 3× NVIDIA RTX 3090 (1× Ti) | ## Caveats - Single class only (`drone`); doesn't distinguish drone subtypes - Trained on YouTube-sourced RGB drone images; recall drops on long-range surveillance (Anti-UAV-RGBT shows ~76% recall on small/distant drones) - Pair with a tracker (ByteTrack or BoT-SORT) for trajectory output ## License Apache 2.0.

Source context: 60 downloads · 2 likes · Pipeline object-detection · Library ultralytics · Repo sapoepsilon/yolov11s-drone-detector

Source context: 56 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo sapoepsilon/yolov11s-drone-detector