  Contemporary person re-identification (\reid) methods usually require access
to data from the deployment camera network during training in order to perform
well. This is because contemporary \reid{} models trained on one dataset do not
generalise to other camera networks due to the domain-shift between datasets.
This requirement is often the bottleneck for deploying \reid{} systems in
practical security or commercial applications, as it may be impossible to
collect this data in advance or prohibitively costly to annotate it. This paper
alleviates this issue by proposing a simple baseline for domain
generalizable~(DG) person re-identification. That is, to learn a \reid{} model
from a set of source domains that is suitable for application to unseen
datasets out-of-the-box, without any model updating. Specifically, we observe
that the domain discrepancy in \reid{} is due to style and content variance
across datasets and demonstrate appropriate Instance and Feature Normalization
alleviates much of the resulting domain-shift in Deep \reid{} models. Instance
Normalization~(IN) in early layers filters out style statistic variations and
Feature Normalization~(FN) in deep layers is able to further eliminate
disparity in content statistics. Compared to contemporary alternatives, this
approach is extremely simple to implement, while being faster to train and
test, thus making it an extremely valuable baseline for implementing \reid{} in
practice. With a few lines of code, it increases the rank 1 \reid{} accuracy by
{11.8\%, 33.2\%, 12.8\% and 8.5\%} on the VIPeR, PRID, GRID, and i-LIDS
benchmarks respectively. Source codes are available at
\url{https://github.com/BJTUJia/person_reID_DualNorm}.
