Abstract
To this date, scanning electron microscope has produced among the most complex and diverse images at nanoscale resolution. The highly magnified images of backscattered electrons reflected from the surface of samples are non-uniformed, even for the same class of images. The study investigates the impact of having a small but diverse dataset on the performance of AlexNet. A total of 160 samples from EUDAT Collaborative Database Infrastructure is used for the study. Compared to the use of new non-augmented samples to increase the size of dataset, image augmentation has been significantly improved classification performance and generalization ability of the AlexNet.
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