Artificial intelligence offers new ways to organize and share cultural heritage collections. This paper evaluates its potential for automatically classifying paintings by art period, as a tool for the digitization and accessibility of art-related information. Two convolutional neural network (CNN) models, VGG16 and MobileNetV2, were adapted through transfer learning from ImageNet-pretrained weights and trained on the WikiArt dataset, which contains 81,444 paintings across 27 art periods. Class imbalance was addressed with data augmentation and class weights, and the data were split into stratified training, validation and test sets (70/15/15). VGG16 achieved the best performance, with an AUC of 0.966, an accuracy of 0.763 and an F1-score of 0.701, compared with 0.946, 0.703 and 0.699 for MobileNetV2. MobileNetV2, however, trained faster and used less memory. The results reveal a trade-off between accuracy and efficiency that can guide the choice of model for galleries, museums and educational platforms.
Keywords: Computer Vision, MobileNetV2, VGG16, art period classification, convolutional neural networks, transfer learning
