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Learn Yolo Computer Vision Tutorial, validate concepts with Yolo Computer Vision MCQ Questions, and prepare interviews through Yolo Computer Vision Interview Questions and Answers.
YOLO (You Only Look Once) MCQ
Dense prediction from one (or few) forward passes: cells, priors, objectness, and why YOLO prioritizes speed.
One pass
Single shot
Grid
Cells
Anchors
Priors
FPS
Latency
YOLO overview
YOLO frames detection as regression from a grid: each cell predicts a small number of boxes and class probabilities. Later versions add better anchors, FPN-like paths, and improved training losses.
Why fast?
One CNN evaluation produces all predictions—amenable to GPU batching and video throughput.
Concepts
Cell responsibility
Training assigns objects to cells (by center); limits duplicates per cell in early YOLO.
Versions
v3/v5/v8 etc. differ in anchor strategies, loss, and architecture—principles remain dense prediction.
Tradeoff
Higher FPS often sacrifices some mAP vs heavy two-stage on hard datasets.
Crowding
Many overlapping same-class objects stress anchor/grid assignment—NMS and architecture matter.
Idea
Image → CNN → tensors of box + class predictions