  To make the best use of the underlying minute and subtle differences,
fine-grained classifiers collect information about inter-class variations. The
task is very challenging due to the small differences between the colors,
viewpoint, and structure in the same class entities. The classification becomes
more difficult due to the similarities between the differences in viewpoint
with other classes and differences with its own. In this work, we investigate
the performance of the landmark general CNN classifiers, which presented
top-notch results on large scale classification datasets, on the fine-grained
datasets, and compare it against state-of-the-art fine-grained classifiers. In
this paper, we pose two specific questions: (i) Do the general CNN classifiers
achieve comparable results to fine-grained classifiers? (ii) Do general CNN
classifiers require any specific information to improve upon the fine-grained
ones? Throughout this work, we train the general CNN classifiers without
introducing any aspect that is specific to fine-grained datasets. We show an
extensive evaluation on six datasets to determine whether the fine-grained
classifier is able to elevate the baseline in their experiments.
