  We use a continuous depth version of the Residual Network (ResNet) model
known as Neural ordinary differential equations (NODE) for the purpose of
galaxy morphology classification. We applied this method to carry out
supervised classification of galaxy images from the Galaxy Zoo 2 dataset, into
five distinct classes, and obtained an accuracy of about 92% for most of the
classes. Through our experiments, we show that NODE not only performs as well
as other deep neural networks, but has additional advantages over them, which
can prove very useful for next generation surveys. We also compare our result
against ResNet. While ResNet and its variants suffer problems, such as time
consuming architecture selection (e.g. the number of layers) and the
requirement of large data for training, NODE does not have these requirements.
Through various metrics, we conclude that the performance of NODE matches that
of other models, despite using only one-third of the total number of parameters
as compared to these other models.
