Guide-linked quiz · Medium Level

Non-Maximum Suppression Quiz

Review greedy NMS, Intersection over Union, confidence ordering, crowded-scene threshold tradeoffs, and soft-score suppression.

Related guide pathsNon Maximum Suppression
Question 1 of 8

What is the main purpose of Non-Maximum Suppression in object detection?

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  1. What is the main purpose of Non-Maximum Suppression in object detection?
    • To remove redundant overlapping boxes, keeping one per object
    • To increase the number of detected boxes
    • To resize the input image
    • To train the detector's weights
  2. Which metric does standard NMS use to decide whether two boxes overlap too much?
    • Euclidean distance between centers
    • Intersection over Union (IoU)
    • Cosine similarity of features
    • Pixel color difference
  3. In greedy NMS, which box is selected first in each round?
    • The largest box by area
    • The box closest to the image center
    • The box with the highest confidence score
    • A randomly chosen box
  4. What is a known weakness of greedy NMS in crowded scenes?
    • It cannot handle color images
    • It always produces too few boxes overall
    • It requires GPU training to run
    • It may suppress a genuine nearby object that overlaps a kept box
  5. How does Soft-NMS differ from standard NMS?
    • It decays the scores of overlapping boxes instead of removing them outright
    • It deletes all boxes regardless of score
    • It ignores confidence scores entirely
    • It only works during training
  6. Why is NMS typically applied separately for each object class?
    • Because each class needs a different image resolution
    • So a box of one class doesn't suppress a box of a different class
    • To make the algorithm slower on purpose
    • Because IoU cannot be computed across classes
  7. How is Intersection over Union (IoU) calculated for two detection boxes?
    • Union area divided by the intersection area
    • The sum of both box perimeters
    • Intersection area divided by the union area
    • The distance between the box centers
  8. What is a typical effect of lowering the IoU threshold in greedy NMS?
    • Fewer boxes are suppressed in all cases
    • The detector retrains its class labels
    • The confidence scores are converted into image pixels
    • More overlapping boxes are suppressed, which can risk removing nearby real objects

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