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Automated photo tagging has established itself as one of the most compellingapplications of deep learning. While deep convolutional neural networks haverepeatedly demonstrated top performance on standard datasets forclassification, there are a number of often overlooked but importantconsiderations when deploying this technology in a real-world scenario. In thispaper, we present our efforts in developing a large-scale photo tagging systemfor Flickr photo search. We discuss topics including how to 1) select the tagsthat matter most to our users; 2) develop lightweight, high-performance modelsfor tag prediction; and 3) leverage the power of large amounts of noisy datafor training. Our results demonstrate that, for real-world datasets, trainingexclusively with this noisy data yields performance on par with the standardparadigm 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 betterfine-tuning results when a small amount of clean data is available. As such, weadvocate for the approach of harnessing user-generated data in large-scalesystems.

Tag Prediction at Flickr: a View from the Darkroom