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A detector returns a category label and a rectangle around each visible item. What task is it performing?

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  1. A detector returns a category label and a rectangle around each visible item. What task is it performing?
    • Object detection
    • Image classification only
    • Pixel-level segmentation only
    • Terjemahan teks
  2. What does image classification often do without object detection?
    • Return a bounding box for each instance
    • Assign a label without locating the object
    • Segment each object pixel
    • Measure box overlap
  3. Why define annotation rules for partly hidden or tiny objects?
    • Small objects do not affect evaluation
    • The rules guarantee perfect accuracy
    • Inconsistent boxes or omitted objects can confuse training and evaluation
    • They determine image resolution
  4. What can a score threshold change in detector postprocessing?
    • The crop’s lighting
    • The image’s original dimensions
    • The ground-truth labels
    • The balance between missed objects and false detections
  5. Two 10-by-10 boxes overlap over 50 pixels; their union is 150 pixels. What is IoU?
    • 0.50
    • About 0.33
    • 0.67
    • 1.00
  6. Why do matching rules matter when reporting precision and recall?
    • They decide category names after training
    • They set camera exposure
    • The match threshold and included object sizes affect which predictions count
    • They remove the need for references
  7. A detector works on large isolated products but misses small crowded items. What should be tested?
    • Real capture conditions, including crowding and occlusion
    • Only a clean laboratory image
    • Only the average score
    • The model’s name
  8. A low-confidence detection could trigger a consequential action. What should be defined beforehand?
    • An automatic action with no review
    • An assumption that every box is correct
    • A rule to ignore all low scores
    • A human review process for uncertain detections