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RetinaNet MCQ
Focal loss down-weights easy negatives; FPN gives pyramidal features—closing the gap toward two-stage mAP.
Focal loss
Easy ↓
FPN
P3–P7
Imbalance
BG dom.
One-stage
Dense
RetinaNet
One-stage detectors face extreme foreground/background imbalance (many easy negatives). Focal loss modulates CE with a focusing term so training emphasizes hard examples.
Focal loss form
FL = −(1 − p_t)^γ log(p_t) (conceptually)—γ reduces loss for well-classified examples.
Components
Dense anchors
FPN levels P3–P7 cover scales; each location has multiple aspect ratios.
vs CE
Plain CE is overwhelmed by easy negatives; focal reweights dynamically.
Subnets
Classification and box regression heads share FPN features.
Impact
Showed one-stage could approach two-stage accuracy with proper loss.
Pyramid
Backbone → FPN → (cls, reg) at each level