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.
Question 1 of 8
What is the main purpose of Non-Maximum Suppression in object detection?
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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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