  The tremendous success of ImageNet-trained deep features on a wide range of
transfer tasks begs the question: what are the properties of the ImageNet
dataset that are critical for learning good, general-purpose features? This
work provides an empirical investigation of various facets of this question: Is
more pre-training data always better? How does feature quality depend on the
number of training examples per class? Does adding more object classes improve
performance? For the same data budget, how should the data be split into
classes? Is fine-grained recognition necessary for learning good features?
Given the same number of training classes, is it better to have coarse classes
or fine-grained classes? Which is better: more classes or more examples per
class? To answer these and related questions, we pre-trained CNN features on
various subsets of the ImageNet dataset and evaluated transfer performance on
PASCAL detection, PASCAL action classification, and SUN scene classification
tasks. Our overall findings suggest that most changes in the choice of
pre-training data long thought to be critical do not significantly affect
transfer performance.? Given the same number of training classes, is it better
to have coarse classes or fine-grained classes? Which is better: more classes
or more examples per class?
