  Quantification is the task of estimating the class-distribution of a
data-set. While typically considered as a parameter estimation problem with
strict assumptions on the data-set shift, we consider quantification
in-the-wild, on two large scale data-sets from marine ecology: a survey of
Caribbean coral reefs, and a plankton time series from Martha's Vineyard
Coastal Observatory. We investigate several quantification methods from the
literature and indicate opportunities for future work. In particular, we show
that a deep neural network can be fine-tuned on a very limited amount of data
(25 - 100 samples) to outperform alternative methods.
