  Inspired by the conclusion that humans choose the visual cortex regions
corresponding to the real size of an object to analyze its features when
identifying objects in the real world, this paper presents a framework,
SizeNet, which is based on both the real sizes and features of objects to solve
object recognition problems. SizeNet was used for object recognition
experiments on the homemade Rsize dataset, and was compared with the
state-of-the-art methods AlexNet, VGG-16, Inception V3, Resnet-18, and
DenseNet-121. The results showed that SizeNet provides much higher accuracy
rates for object recognition than the other algorithms. SizeNet can solve the
two problems of correctly recognizing objects with highly similar features but
real sizes that are obviously different from each other, and correctly
distinguishing a target object from interference objects whose real sizes are
obviously different from the target object. This is because SizeNet recognizes
objects based not only on their features, but also on their real size. The real
size of an object can help exclude the interference object's categories whose
real size ranges do not match the real size of the object, which greatly
reduces the object's categories' number in the label set used for the
downstream object recognition based on object features. SizeNet is of great
significance for studying the interpretable computer vision. Our code and
dataset will thus be made public.
