  Person re-identification is indeed a challenging visual recognition task due
to the critical issues of human pose variation, human body occlusion, camera
view variation, etc. To address this, most of the state-of-the-art approaches
are proposed based on deep convolutional neural network (CNN), being leveraged
by its strong feature learning power and classification boundary fitting
capacity. Although the vital role towards person re-identification, how to
build effective CNN baseline model has not been well studied yet. To answer
this open question, we propose 3 good practices in this paper from the
perspectives of adjusting CNN architecture and training procedure. In
particular, they are adding batch normalization after the global pooling layer,
executing identity categorization directly using only one fully-connected, and
using Adam as optimizer. The extensive experiments on 3 widely-used benchmark
datasets demonstrate that, our propositions essentially facilitate the CNN
baseline model to achieve the state-of-the-art performance without any other
high-level domain knowledge or low-level technical trick.
