Instagram has become a great venue for amateur and professional photographers alike to showcase their work . 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 . 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 . 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 are happy to share our findings with our new research. We hope to use our new data to improve the accuracy of our models to improve our understanding of our analysis of our new models and our ability to predict engagement and to use the data to identify and annotate

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Keywords : engagement - style - instagram - models - trained -

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