Appsthetics is a neural network model developed with the use of the fast.ai library to predict the visual aesthetic rating of mobile Android applications' user interfaces.

The present Jupyter Notebook contains all of the code, training and the results achieved by the model and is structured as such:

This section takes care of all the initializations needed, from checking if the current machine's specs are adequate to setting up your Google Drive as a virtual disk so the Notebook can read from and write to it. Library imports and all the prep work for the dataset also takes place in here.

This was the starting point of Appsthetics. We load up the ResNet34 model and perform trainings, fine-tunings and evaluate the results for the ResNet34 in this section.

This section covers the training, fine-tunings and results of the ResNet18 model.

This section covers the training, fine-tunings and results of the ResNet50 and ResNet101 models.

This is a section with notes of the best achieved results for each model trained. For quick future reference.

This section contains some work done for the Bland & Altman test and other things.