  Automated photo tagging has established itself as one of the most compelling
applications of deep learning. While deep convolutional neural networks have
repeatedly demonstrated top performance on standard datasets for
classification, there are a number of often overlooked but important
considerations when deploying this technology in a real-world scenario. In this
paper, we present our efforts in developing a large-scale photo tagging system
for Flickr photo search. We discuss topics including how to 1) select the tags
that matter most to our users; 2) develop lightweight, high-performance models
for tag prediction; and 3) leverage the power of large amounts of noisy data
for training. Our results demonstrate that, for real-world datasets, training
exclusively with this noisy data yields performance on par with the standard
paradigm of first pre-training on clean data and then fine-tuning. In addition,
we observe that the models trained with user-generated data can yield better
fine-tuning results when a small amount of clean data is available. As such, we
advocate for the approach of harnessing user-generated data in large-scale
systems.
