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질문 1 의 8
A detector returns a category label and a rectangle around each visible item. What task is it performing?
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- 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
- 텍스트 번역
- 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
- 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
- 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
- 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
- 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
- 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
- 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