  This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images
of aircraft spanning 100 aircraft models, organised in a three-level hierarchy.
At the finer level, differences between models are often subtle but always
visually measurable, making visual recognition challenging but possible. A
benchmark is obtained by defining corresponding classification tasks and
evaluation protocols, and baseline results are presented. The construction of
this dataset was made possible by the work of aircraft enthusiasts, a strategy
that can extend to the study of number of other object classes. Compared to the
domains usually considered in fine-grained visual classification (FGVC), for
example animals, aircraft are rigid and hence less deformable. They, however,
present other interesting modes of variation, including purpose, size,
designation, structure, historical style, and branding.
