  One of the most important magnetic spin structure is the topologically
stabilised skyrmion quasi-particle. Its interesting physical properties make
them candidates for memory and efficient neuromorphic computation schemes. For
the device operation, detection of the position, shape, and size of skyrmions
is required and magnetic imaging is typically employed. A frequently used
technique is magneto-optical Kerr microscopy where depending on the samples
material composition, temperature, material growing procedures, etc., the
measurements suffer from noise, low-contrast, intensity gradients, or other
optical artifacts. Conventional image analysis packages require manual
treatment, and a more automatic solution is required. We report a convolutional
neural network specifically designed for segmentation problems to detect the
position and shape of skyrmions in our measurements. The network is tuned using
selected techniques to optimize predictions and in particular the number of
detected classes is found to govern the performance. The results of this study
shows that a well-trained network is a viable method of automating data
pre-processing in magnetic microscopy. The approach is easily extendable to
other spin structures and other magnetic imaging methods.
