  Equaixed dendrites are frequently encountered in solidification. They
typically form in large numbers, which makes their detection, localization, and
tracking practically impossible for a human eye. In this paper, we show how
recent progress in the field of machine learning can be leveraged to tackle
this problem and we present computer vision neural network to automatically
detect equiaxed dendrites. Our network is trained using phase-field simulation
results, and proper data augmentation allows to perform the detection task in
solidification conditions entirely different from those simulated for training.
For example, here we show how they can successfully detect dendrites of various
sizes in a microgravity solidification experiment. We discuss challenges in
training such a network along with our solutions for them, and compare the
performance of neural network with traditional methods of shapes detection.
