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🚀 Open-sourcing the UAVDT Object Detection Model Zoo on Hugging Face.
- 🤖 18 models: YOLOv8/v9/v10/YOLO11/YOLO26 (n–m) and RF-DETR Nano/Small/Medium.
- 🚁 Drone traffic surveillance: cars, trucks and buses, mostly tiny objects (median box ~0.14% of the image).
- 📊 Model cards with metrics, per-class results, curves, showcases, demo videos and full configs.
Headline numbers:
- 🏆 Best mAP@50: 33.43% (YOLOv26m). Best mAP@50:95: 20.54% (RF-DETR Medium).
- ⚡ YOLOv26s gets 32.98% mAP@50 at 22.8 GFLOPs, ~3.3x fewer than YOLOv26m.
Trained and evaluated with DetectionBench: https://github.com/dronefreak/DetectionBench
Dataset credit: Dawei Du et al. (ECCV 2018). It is research-only, so I don't mirror it; the dataset repo is a guide to the official source.
📦 Dataset: dronefreak/UAVDT
🤖 Collection: dronefreak/uavdt-object-detection-model-zoo-6aadb3673084702d3ed45ff0
Feedback and contributions welcome.
- 🤖 18 models: YOLOv8/v9/v10/YOLO11/YOLO26 (n–m) and RF-DETR Nano/Small/Medium.
- 🚁 Drone traffic surveillance: cars, trucks and buses, mostly tiny objects (median box ~0.14% of the image).
- 📊 Model cards with metrics, per-class results, curves, showcases, demo videos and full configs.
Headline numbers:
- 🏆 Best mAP@50: 33.43% (YOLOv26m). Best mAP@50:95: 20.54% (RF-DETR Medium).
- ⚡ YOLOv26s gets 32.98% mAP@50 at 22.8 GFLOPs, ~3.3x fewer than YOLOv26m.
Trained and evaluated with DetectionBench: https://github.com/dronefreak/DetectionBench
Dataset credit: Dawei Du et al. (ECCV 2018). It is research-only, so I don't mirror it; the dataset repo is a guide to the official source.
📦 Dataset: dronefreak/UAVDT
🤖 Collection: dronefreak/uavdt-object-detection-model-zoo-6aadb3673084702d3ed45ff0
Feedback and contributions welcome.