relatively new to Computer Vision.
Lets say for example, I have a sequence of images of a car driving away from a static camera into the horizon, and I want to use this image set for some bog standard computer vision experimentation (e.g to train a CNN to recognize a car). I label the car in each frame with a rectangular bounding box. At the start of the sequence, the area of annotation required to cover the visible car is approximately 1500x500 pixels. At the end of the sequence, the area is only 3x1 pixels. Does there come a point in this sequence of images, whether by some rule of thumb or by a derivable metric, in which it no longer becomes beneficial to label the car for training purposes (i.e it will harm model performance)? I remember reading somewhere recently that its not beneficial to train CV algorithms on objects that change size, but I'm not sure whether there are guidelines to setting acceptable tolerance thresholds.