  In this paper we introduce a new neural network architecture designed to use
in embedded vision applications. It merges the best working practices of
network architectures like MobileNets and ResNets to our named RMNet
architecture. We also focus on key moments of building mobile architectures to
carry out in the limited computation budget. Additionally, to demonstrate the
effectiveness of our architecture we evaluate the RMNet backbone on Person
Re-identification task. The proposed approach is in top 3 of state of the art
solutions on Market-1501 challenge, however our method significantly
outperforms them by the inference speed.
