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This work considers the problem of domain shift in personre-identification.Being trained on one dataset, a re-identification modelusually performs much worse on unseen data. Partially this gap is caused by therelatively small scale of person re-identification datasets (compared to facerecognition ones, for instance), but it is also related to training objectives.We propose to use the metric learning objective, namely AM-Softmax loss, andsome 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 architectureadjustments to obtain state-of-the art results in cross-domain generalizationproblem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC,MSMT17-train->Market1501 and MSMT17-all->Market1501.

Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning