  This study reports an approach and presents its open-source implementation
for quantitative analysis of experimental flows using streak images and
Convolutional Neural Networks (CNN). The latter are applied to retrieve a
length and an angle from streaks, which can be used to deduce kinetic energy
and directionality (up to 180$^{\circ}$ ambiguity) of an imaged flow. We
developed a quick method for generating essentially unlimited number of
training and validation images, which enabled efficient training. Additionally,
we show how to apply an ensemble of CNNs to derive a formal uncertainty on the
estimated quantities. The approach is validated on the numerical simulation of
a convenctive turbulent flow and applied to a longitutidal libration flow
experiment.
