  Many current successful Person Re-Identification(ReID) methods train a model
with the softmax loss function to classify images of different persons and
obtain the feature vectors at the same time. However, the underlying feature
embedding space is ignored. In this paper, we use a modified softmax function,
termed Sphere Softmax, to solve the classification problem and learn a
hypersphere manifold embedding simultaneously. A balanced sampling strategy is
also introduced. Finally, we propose a convolutional neural network called
SphereReID adopting Sphere Softmax and training a single model end-to-end with
a new warming-up learning rate schedule on four challenging datasets including
Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results
demonstrate that this single model outperforms the state-of-the-art methods on
all four datasets without fine-tuning or re-ranking. For example, it achieves
94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on
DukeMTMC-reID. The code and trained weights of our model will be released.
