Art Classification with Pytorch Using Transfer Learning

Abstract

Deep Learning has advanced to a greater level in the field of Artificial Intelligence in recent years, and it is currently employed globally. This aids the system in improving its accuracy. Deep Learning algorithms have made image classification considerably more viable, allowing us to analyse large datasets. Deep Convolutional Neural Networks are used in the majority of image classification nowadays. In this paper, Image Classification is performed using the VGG16, ResNet18, ResNet50, GoogleNet, MobileNet, AlexNet in Best Artworks of All Time Dataset which is taken from the Kaggle and the best model for training the dataset is choosen. This Dataset is the collection of the 8355 high resolution portraits which is in form of the RGB Images. After experimentation it is found that, in the Best Artworks of all Time data the ResNet50 achieved better accuracy of 87.15% and loss of 0.0015% among all other trained Deep Networks.