Identifying Artists from Paintings Using Deep Learning Models

Identifying artists from paintings automatically has become more crucial as art collections go digital. This research compares how effective ResNet 50, ResNet 152, MobileNet v2 and Inception v3 architectures are for this purpose. In order to stress the importance of transfer learning in the classification of artworks, we use a dataset that is scraped from “Art Challenge-A Quiz Game of Famous Painters”. By conducting experiments, we assess each model's accuracy, computational efficiency and interpretability. Our discoveries help progress automated art analysis and provide insights, for researchers and professionals dealing with digital art collections.