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R-CNN Family MCQ
From warped region CNNs to shared-feature ROI pooling and learned proposals—why two-stage was accurate before one-stage speed wars.
Regions
Warp
ROI pool
Fixed grid
Fast
Shared conv
RPN
Anchors
R-CNN family
R-CNN ran a CNN on thousands of warped proposal windows—slow. Fast R-CNN runs CNN once, pools features per ROI. Faster R-CNN learns proposals with a Region Proposal Network on feature maps.
ROI Pooling
Quantizes each ROI onto the feature grid into a fixed H×W—enables batching FC heads (RoIAlign refines alignment).
Evolution
R-CNN
Proposals + per-ROI forward—redundant compute.
Fast
Shared backbone; ROI pooling gathers features per proposal.
Faster
RPN classifies objectness + regresses boxes from anchors on the feature map.
Tradeoff
Two-stage: often higher mAP; heavier latency than one-stage YOLO-style.
Faster R-CNN
Backbone → FPN (optional) → RPN → RoI head (cls + reg [+ mask])