In some computer vision domains, such as medical or hyperspectral imaging, we
care about the classification of tiny objects in large images. However, most
Convolutional Neural Networks (CNNs) for image classification were developed
and analyzed using biased datasets that contain large objects, most often, in
central image positions. To assess whether classical CNN architectures work
well for tiny object classification we build a comprehensive testbed containing
two datasets: one derived from MNIST digits and other from histopathology
images. This testbed allows us to perform controlled experiments to stress-test
CNN architectures using a broad spectrum of signal-to-noise ratios. Our
observations suggest that: (1) There exists a limit to signal-to-noise below
which CNNs fail to generalize and that this limit is affected by dataset size -
more data leading to better performances; however, the amount of training data
required for the model to generalize scales rapidly with the inverse of the
object-to-image ratio (2) in general, higher capacity models exhibit better
generalization; (3) when knowing the approximate object sizes, adapting
receptive field is beneficial; and (4) for very small signal-to-noise ratio the
choice of global pooling operation affects optimization, whereas for relatively
large signal-to-noise values, all tested global pooling operations exhibit
similar performance.