  In recent years, large pre-trained deep neural networks (DNNs) have
revolutionized the field of computer vision (CV). Although these DNNs have been
shown to be very well suited for general image recognition tasks, application
in industry is often precluded for three reasons: 1) large pre-trained DNNs are
built on hundreds of millions of parameters, making deployment on many devices
impossible, 2) the underlying dataset for pre-training consists of general
objects, while industrial cases often consist of very specific objects, such as
structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal
issues for companies. As a remedy, we study neural networks for CV that we
train from scratch. For this purpose, we use a real-world case from a solar
wafer manufacturer. We find that our neural networks achieve similar
performances as pre-trained DNNs, even though they consist of far fewer
parameters and do not rely on third-party datasets.
