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 Advanced
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1. YOLO core idea
2. S×S grid
3. YOLOv1 loss terms
4. Anchors (v2+)
5. IoU in training
6. NMS post-process
7. Objectness
8. Multi-scale heads
9. FPN-like in YOLO
10. YOLOv5/v8 ecosystem
11. Export ONNX / TensorRT
12. Small objects
13. Crowded scenes
14. Augmentation
15. mAP vs FPS
16. YOLO vs SSD
17. YOLO vs RetinaNet
18. Tiling large images
19. Rotated boxes (note)
20. Edge deployment
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

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