  Multi-label image classification allows predicting a set of labels from a
given image. Unlike multiclass classification, where only one label per image
is assigned, such a setup is applicable for a broader range of applications. In
this work we revisit two popular approaches to multilabel classification:
transformer-based heads and labels relations information graph processing
branches. Although transformer-based heads are considered to achieve better
results than graph-based branches, we argue that with the proper training
strategy, graph-based methods can demonstrate just a small accuracy drop, while
spending less computational resources on inference. In our training strategy,
instead of Asymmetric Loss (ASL), which is the de-facto standard for multilabel
classification, we introduce its metric learning modification. In each binary
classification sub-problem it operates with $L_2$ normalized feature vectors
coming from a backbone and enforces angles between the normalized
representations of positive and negative samples to be as large as possible.
This results in providing a better discrimination ability, than binary cross
entropy loss does on unnormalized features. With the proposed loss and training
strategy, we obtain SOTA results among single modality methods on widespread
multilabel classification benchmarks such as MS-COCO, PASCAL-VOC, NUS-Wide and
Visual Genome 500. Source code of our method is available as a part of the
OpenVINO Training Extensions
https://github.com/openvinotoolkit/deep-object-reid/tree/multilabel
