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1. آبجیکٹ کا پتہ لگانا تصویر کی درجہ بندی سے کیسے مختلف ہے؟
   • 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. تصویر کی تقسیم کیا پیدا کرتی ہے؟
   • 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. ایک درجہ بندی کرنے والا کیا غلط ہو سکتا ہے یہاں تک کہ جب اس کا امیج لیبل پراعتماد دکھائی دے؟
   • 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. ڈیٹا اسپلٹ میں ایک ہی منظر سے متعلقہ تصاویر یا ریکارڈنگ کو ایک ساتھ کیوں رکھیں؟
   • 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. کٹائی تصویری ماڈل کو کیسے متاثر کر سکتی ہے؟
   • 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