We investigate recognition performance between people and deep learning techniques using a simple task of visual inspection where non-experts can detect anomalies. Deep learning techniques are frequently applied to automatic visual inspection. The techniques require to collect a sufficient number of training samples. To evaluate the number of training samples to obtain the recognition performance of human levels on a simple task, we conducted visual inspection experiments using a simple task. We confirmed the different tendencies of training samples between people and deep learning techniques.