  Recent studies on multi-label image classification have focused on designing
more complex architectures of deep neural networks such as the use of attention
mechanisms and region proposal networks. Although performance gains have been
reported, the backbone deep models of the proposed approaches and the
evaluation metrics employed in different works vary, making it difficult to
compare each fairly. Moreover, due to the lack of properly investigated
baselines, the advantage introduced by the proposed techniques are often
ambiguous. To address these issues, we make a thorough investigation of the
mainstream deep convolutional neural network architectures for multi-label
image classification and present a strong baseline. With the use of proper data
augmentation techniques and model ensembles, the basic deep architectures can
achieve better performance than many existing more complex ones on three
benchmark datasets, providing great insight for the future studies on
multi-label image classification.
