  In a fashion e-commerce platform where customers can't physically examine the
products on their own, being able to see other customers' text and image
reviews of the product is critical while making purchase decisions. Given the
high reliance on these reviews, over the years we have observed customers
proactively sharing their reviews. With an increase in the coverage of User
Generated Content (UGC), there has been a corresponding increase in the number
of customer images. It is thus imperative to display the most relevant images
on top as it may influence users' online shopping choices and behavior. In this
paper, we propose a simple yet effective training procedure for ranking
customer images. We created a dataset consisting of Myntra (A Major Indian
Fashion e-commerce company) studio posts and highly engaged (upvotes/downvotes)
UGC images as our starting point and used selected distortion techniques on the
images of the above dataset to bring their quality at par with those of bad UGC
images. We train our network to rank bad-quality images lower than high-quality
ones. Our proposed method outperforms the baseline models on two metrics,
namely correlation coefficient, and accuracy, by substantial margins.
