Computer Vision Interview
20 essential Q&A
Updated 2026
YOLO
YOLO: 20 Essential Q&A
You Only Look Once—grids, anchors, and the push for real-time detection.
~12 min read
20 questions
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1
What does YOLO mean?
📊 medium
Answer: You Only Look Once: single forward pass predicts boxes and classes—treats detection as regression from a grid of cells.
2
YOLOv1 grid idea?
📊 medium
Answer: Image split into S×S cells; cell responsible for object whose center falls in it—predicts B boxes + class distribution per cell.
3
YOLOv1 loss components?
🔥 hard
Answer: Coordinate regression (with sqrt w,h trick), confidence (IoU weighted), classification CE—λ weights balance localization vs no-object cells.
4
When did anchors appear?
📊 medium
Answer: YOLOv2+ uses k-means anchor priors on dataset boxes—predict offsets instead of raw sizes for stability.
5
IoU in training?
📊 medium
Answer: Assign anchors/cells to GT by best IoU; some versions ignore preds below IoU threshold for classification to reduce conflict.
6
Post-processing?
⚡ easy
Answer: Like other detectors: NMS on decoded boxes with class-wise scores—some variants use DIoU-NMS or soft-NMS.
7
Objectness vs class?
⚡ easy
Answer: Objectness = is there an object in this anchor; class = which class—decoupled in many heads (obj * class prob = final score).
8
Multi-scale YOLO?
📊 medium
Answer: Later versions predict at multiple feature map scales (e.g. large/small stride) to catch objects of different sizes—similar spirit to FPN.
9
Path aggregation?
📊 medium
Answer: Models like YOLOv4 use PANet-style bottom-up path after top-down FPN for richer multi-scale features.
10
YOLOv5/v8 / Ultralytics?
⚡ easy
Answer: Popular PyTorch implementations with training zoo, export, and deployment tooling—interview “practical YOLO” often means this ecosystem.
11
Deploy on edge?
📊 medium
Answer: Export to ONNX, TensorRT, CoreML—quantize INT8 for speed; validate mAP drop after conversion.
12
Small objects?
📊 medium
Answer: Higher-res input, smaller stride heads, copy-paste aug, or tiling—same fundamentals as other detectors.
13
Crowded objects?
🔥 hard
Answer: Grid responsibility and NMS can struggle—improved assignment (e.g. ATSS-style ideas in some detectors) and better NMS help.
14
Common augmentations?
📊 medium
Answer: Mosaic, mixup, HSV jitter, random scale—strong aug standard in modern YOLO training recipes.
15
mAP vs FPS tradeoff?
⚡ easy
Answer: Larger model and image size ↑ mAP, ↓ FPS—choose for product SLA (latency vs accuracy).
16
YOLO vs SSD?
📊 medium
Answer: Both one-stage; SSD uses multi-scale default boxes on VGG features; YOLO family evolved different heads and assignment—both real-time capable.
17
YOLO vs RetinaNet?
📊 medium
Answer: RetinaNet introduced focal loss for dense classification imbalance; YOLO uses different obj loss weighting—both dense predictors.
18
Tiling satellite / huge images?
📊 medium
Answer: Split image, run YOLO per tile with overlap, merge + NMS—handle boundary duplicates.
19
Rotated boxes?
🔥 hard
Answer: Variants predict angle θ or use rotated IoU—needed for aerial/text detection.
20
Real-time on CPU?
📊 medium
Answer: Choose nano/tiny backbones, reduce input size, INT8—expect large accuracy gap vs GPU server models.
YOLO Cheat Sheet
Idea
- Single forward
- Dense preds
Train
- Anchors (v2+)
- Multi-scale heads
Ship
- NMS
- TensorRT / ONNX
💡 Pro tip: One-stage = dense predictions + clever assignment + NMS.
Full tutorial track
Go deeper with the matching tutorial chapter and code examples.