Suppose a CNN is trained to detect bounding box of a certain type of object (people, cars, houses, etc.)
If each image in the training set contains just one object (and its corresponding bounding box), how well can a CNN generalize to pick up all objects if the input for prediction contains multiple objects?
Should the training images be downsampled in order for the CNN to pick out multiple objects in the prediction?
I don't have a specific one in mind. I was just curious about the general behavior.