  Instagram has become a great venue for amateur and professional photographers
alike to showcase their work. It has, in other words, democratized photography.
Generally, photographers take thousands of photos in a session, from which they
pick a few to showcase their work on Instagram. Photographers trying to build a
reputation on Instagram have to strike a balance between maximizing their
followers' engagement with their photos, while also maintaining their artistic
style. We used transfer learning to adapt Xception, which is a model for object
recognition trained on the ImageNet dataset, to the task of engagement
prediction and utilized Gram matrices generated from VGG19, another object
recognition model trained on ImageNet, for the task of style similarity
measurement on photos posted on Instagram. Our models can be trained on
individual Instagram accounts to create personalized engagement prediction and
style similarity models. Once trained on their accounts, users can have new
photos sorted based on predicted engagement and style similarity to their
previous work, thus enabling them to upload photos that not only have the
potential to maximize engagement from their followers but also maintain their
style of photography. We trained and validated our models on several Instagram
accounts, showing it to be adept at both tasks, also outperforming several
baseline models and human annotators.
