  We present a dataset consisting of high-resolution images of 13 micro-PCBs
captured in various rotations and perspectives relative to the camera, with
each sample labeled for PCB type, rotation category, and perspective
categories. We then present the design and results of experimentation on
combinations of rotations and perspectives used during training and the
resulting impact on test accuracy. We then show when and how well data
augmentation techniques are capable of simulating rotations vs. perspectives
not present in the training data. We perform all experiments using CNNs with
and without homogeneous vector capsules (HVCs) and investigate and show the
capsules' ability to better encode the equivariance of the sub-components of
the micro-PCBs. The results of our experiments lead us to conclude that
training a neural network equipped with HVCs, capable of modeling equivariance
among sub-components, coupled with training on a diversity of perspectives,
achieves the greatest classification accuracy on micro-PCB data.
