Using Deep Learning to automatically rank millions of hotel images

For each hotel we receive dozens of images and face the challenge of choosing the most "attractive" image for each offer on our offer comparison pages, as photos can be just as important for bookings as reviews. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need an "attractiveness" assessment. We addressed the need to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google's research paper "NIMA: Neural Image Assessment". NIMA consists of two Convolutional Neural Networks (CNN) that aim to predict the aesthetic and technical quality of images, respectively. The models are trained via transfer learning, where ImageNet pre-trained CNNs are fine-tuned for each quality classification tasks.