See pdf version of code here.
Builds and trains a neural network that assigns aesthetic quality scores to images. It is trained on images from the AVA (Aesthetic Visual Analysis) dataset, available here.
It first uses the VGG19 network to convert images from the AVA dataset into codes, then runs the codes through a fully-connected network with two hidden layers, to return image ratings. The rating for an image is in the form of a probability distribution over the integers from 1 (lowest quality) to 10 (highest quality).
The network achieves a correlation coefficient of 0.52 between the predicted and actual ratings for a validation set of images. Additionally, I do a factor analysis to determine which VGG19 codes have the largest effect on image quality. The notebook includes functions to show a selection of images with high values of any VGG19 code.
The ipynb is a file you can run on your computer using Jupyter Notebook. If you run the notebook, you will need the AVA dataset on your local computer. Note the hardcoded location ("E:/AVA_dataset") in the python code.