feat: Feature/yolo

[dronefreak]

Models

Model	Type	Notes
fasterrcnn_resnet50 / fasterrcnn_mobilenet	Torchvision	Best accuracy / lightweight
fcos_resnet50	Torchvision	Anchor-free
retinanet_resnet50	Torchvision	Focal loss, class imbalance
yolov8n/s/m/l/x	YOLO v8	Recommended for new experiments
yolov9c/e/m	YOLO v9	Programmable gradient info
yolov10n/s/m/b/l/x	YOLO v10	NMS-free inference
yolo11n/s/m/l/x	YOLO 11	2024 C3k2+C2PSA architecture
yolo26n/s/m/l/x	YOLO 26	2025, best efficiency

python scripts/train.py --available-models   # list all 33 models

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Usage

Train

# Torchvision (Faster R-CNN)
python scripts/train.py \
    --train-img-dir data/VisDrone2019-DET-train/images \
    --train-ann-dir data/VisDrone2019-DET-train/annotations \
    --val-img-dir   data/VisDrone2019-DET-val/images \
    --val-ann-dir   data/VisDrone2019-DET-val/annotations \
    --model fasterrcnn_resnet50 --epochs 200 --batch-size 2 \
    --amp --augmentation --multiscale --small-anchors \
    --lr 0.005 --lr-schedule multistep --lr-milestones 60 80 \
    --output-dir outputs/fasterrcnn_200ep

# YOLO (delegates to Ultralytics engine)
python scripts/train.py \
    --train-img-dir data/VisDrone2019-DET-train/images \
    --train-ann-dir data/VisDrone2019-DET-train/annotations \
    --val-img-dir   data/VisDrone2019-DET-val/images \
    --val-ann-dir   data/VisDrone2019-DET-val/annotations \
    --model yolov8n --epochs 200 --batch-size 16 --amp \
    --output-dir outputs/yolov8n_200ep