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In some important computer vision domains, such as medical or hyperspectralimaging, we care about the classification of tiny objects in large images.However, most Convolutional Neural Networks (CNNs) for image classificationwere developed using biased datasets that contain large objects, in mostlycentral image positions. To assess whether classical CNN architectures workwell for tiny object classification we build a comprehensive testbed containingtwo datasets: one derived from MNIST digits and one from histopathology images.This testbed allows controlled experiments to stress-test CNN architectureswith a broad spectrum of signal-to-noise ratios. Our observations indicatethat: (1) There exists a limit to signal-to-noise below which CNNs fail togeneralize and that this limit is affected by dataset size - more data leadingto better performances; however, the amount of training data required for themodel to generalize scales rapidly with the inverse of the object-to-imageratio (2) in general, higher capacity models exhibit better generalization; (3)when knowing the approximate object sizes, adapting receptive field isbeneficial; and (4) for very small signal-to-noise ratio the choice of globalpooling operation affects optimization, whereas for relatively largesignal-to-noise values, all tested global pooling operations exhibit similarperformance.

Needles in Haystacks: On Classifying Tiny Objects in Large Images