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

This work considers the problem of domain shift in person re-identification. Being trained on one dataset, a reidentification 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. Training code and the models are available online in the GitHub repository11https://github.com/openvinotoolkit/training_extensions/tree/develop/pytorch_toolkit/object_reidentitication/person_reidentitication.