  We review three recently-proposed classifier quality metrics and consider
their suitability for large-scale classification challenges such as applying
convolutional neural networks to the 1000-class ImageNet dataset. These
metrics, referred to as the "geometric accuracy," "decisiveness," and
"robustness," are based on the generalized mean ($\rho$ equals 0, 1, and -2/3,
respectively) of the classifier's self-reported and measured probabilities of
correct classification. We also propose some minor clarifications to
standardize the metric definitions. With these updates, we show some examples
of calculating the metrics using deep convolutional neural networks (AlexNet
and DenseNet) acting on large datasets (the German Traffic Sign Recognition
Benchmark and ImageNet).
