This study aims to propose a paper classification method by gloss unevenness that can apply improvement of printing quality. At first, we take gloss images of coated papers that are same grade and different brands. After that, we classify them by contrastive learning. These accuracies are compared with supervised learning. Contrastive learning methods that we used in this study used Resnet-34 for CNN. As a result, they can classify approximately 53% rate. We will try implementing classification by voting for higher accuracy.
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