  This work considers the problem of domain shift in person
re-identification.Being trained on one dataset, a re-identification model
usually performs much worse on unseen data. Partially this gap is caused by the
relatively small scale of person re-identification datasets (compared to face
recognition ones, for instance), but it is also related to training objectives.
We propose to use the metric learning objective, namely AM-Softmax loss, and
some additional training practices to build well-generalizing, yet,
computationally efficient models. We use recently proposed Omni-Scale Network
(OSNet) architecture combined with several training tricks and architecture
adjustments to obtain state-of-the art results in cross-domain generalization
problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC,
MSMT17-train->Market1501 and MSMT17-all->Market1501.
