  An effective person re-identification (re-ID) model should learn feature
representations that are both discriminative, for distinguishing
similar-looking people, and generalisable, for deployment across datasets
without any adaptation. In this paper, we develop novel CNN architectures to
address both challenges. First, we present a re-ID CNN termed omni-scale
network (OSNet) to learn features that not only capture different spatial
scales but also encapsulate a synergistic combination of multiple scales,
namely omni-scale features. The basic building block consists of multiple
convolutional streams, each detecting features at a certain scale. For
omni-scale feature learning, a unified aggregation gate is introduced to
dynamically fuse multi-scale features with channel-wise weights. OSNet is
lightweight as its building blocks comprise factorised convolutions. Second, to
improve generalisable feature learning, we introduce instance normalisation
(IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to
determine the optimal placements of these IN layers in the architecture, we
formulate an efficient differentiable architecture search algorithm. Extensive
experiments show that, in the conventional same-dataset setting, OSNet achieves
state-of-the-art performance, despite being much smaller than existing re-ID
models. In the more challenging yet practical cross-dataset setting, OSNet
beats most recent unsupervised domain adaptation methods without using any
target data. Our code and models are released at
\texttt{https://github.com/KaiyangZhou/deep-person-reid}.
