The goal of this project is to implement Deep Learning models based on CNN capable of recognizing the author of a painting starting from its characteristics, such as color and style.
The dataset used for the project has been downloaded from Kaggle. It contains the paintings of 50 international painters, for a total of 8446 paintings. The paintings differ greatly from each other in size, but also in color and style.
Different approaches have been tested to achieve the objective: a CNN from scratch and several CNN in Fine Tuning.
The first approach saw the implementation of a simple CNN from scratch. As the network has produced poor results, it has been optimized through a Bayesian search of hyper-parameters. For this purpose, it has been used the Python library keras_tuner. Once it has been defined a search space for the model's hyperparameters, the library allows to find the optimal configuration with respect to a given parameter (validation accuracy or validation loss).
We considered three different known architectures such as VGG-16, MobileNet-v2 and ResNet-50. For each network we excluded the last FCC layers and we added an input layer and a pre-processing layer.
Furthermore, we added some data augmentation layers, such as RandomFlip, RandomRotation, RandomContrast and RandomZoom, to improve the results. However, we discovered that the data augmentation affects negatively the results. We assumed that it is due to the fact that the excessive modification of the painting does not allow the correct painter to be associated with it.
In the end we decided to optimize the model based on VGG-16 with a Bayesian search of the hyper-parameters through the libraty BayesianOptimization. The final results improved, but not as much as we hoped.