  Face recognition has been an active and vital topic among computer vision
community for a long time. Previous researches mainly focus on loss functions
used for facial feature extraction network, among which the improvements of
softmax-based loss functions greatly promote the performance of face
recognition. However, the contradiction between the drastically increasing
number of face identities and the shortage of GPU memories is gradually
becoming irreconcilable. In this paper, we thoroughly analyze the optimization
goal of softmax-based loss functions and the difficulty of training massive
identities. We find that the importance of negative classes in softmax function
in face representation learning is not as high as we previously thought. The
experiment demonstrates no loss of accuracy when training with only 10\%
randomly sampled classes for the softmax-based loss functions, compared with
training with full classes using state-of-the-art models on mainstream
benchmarks. We also implement a very efficient distributed sampling algorithm,
taking into account model accuracy and training efficiency, which uses only
eight NVIDIA RTX2080Ti to complete classification tasks with tens of millions
of identities. The code of this paper has been made available
https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc.
