Computer Vision adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Bawo ni wiwa nkan ṣe yatọ si isọdi aworan?
   • Classification locates each object with a box, while detection assigns one label to the whole image
   • Classification and detection both return one label for the whole image
   • Detection finds object locations with labels, while classification assigns an image-level label
   • Classification labels pixels, while detection labels regions without locations
2. What does image segmentation produce?
   • One label for the whole image
   • A set of bounding boxes around object instances
   • Labels assigned to pixels or regions
   • Class scores without spatial assignments
3. What can a classifier get wrong even when its image label appears confident?
   • It may rely on a background shortcut rather than the intended object
   • It always locates every object boundary
   • It necessarily checks every relevant condition
   • It proves that the scene is safe
4. Why keep related images from the same scene or recording together in a data split?
   • To balance image labels across training and test sets
   • To prevent near-duplicate scene information from appearing in both partitions
   • To place every image from each category into the test set
   • To increase visual variation in the training examples
5. How can cropping affect an image model?
   • It can remove small objects or useful context before inference
   • It preserves every small detail after compression
   • It automatically reconstructs content outside the cropped area
   • It changes an image classifier into a segmentation model
6. Which evaluation best reflects how a vision model will be used?
   • Only clean studio images from training
   • Realistic camera, lighting, resolution, viewpoint, and environment conditions
   • Only images from one familiar location
   • Only the model’s confidence scores
7. Why should a vision team consider the cost of different mistakes before deployment?
   • False alarms and missed objects have equal consequences in every application
   • The model’s confidence score already incorporates every operational cost
   • Error costs matter only while training, not when the system is used
   • Different errors can have different consequences in the intended application
8. Does a confident visual label prove that a scene is safe?
   • No; confidence is a model score, while safety depends on context, error consequences, and appropriate controls
   • Yes; a sufficiently high confidence score establishes safe use in every setting
   • Yes; strong image-level accuracy proves that each individual prediction is safe
   • Only when the model labels pixels rather than assigning image-level categories