  In this paper, we develop face.evoLVe -- a comprehensive library that
collects and implements a wide range of popular deep learning-based methods for
face recognition. First of all, face.evoLVe is composed of key components that
cover the full process of face analytics, including face alignment, data
processing, various backbones, losses, and alternatives with bags of tricks for
improving performance. Later, face.evoLVe supports multi-GPU training on top of
different deep learning platforms, such as PyTorch and PaddlePaddle, which
facilitates researchers to work on both large-scale datasets with millions of
images and low-shot counterparts with limited well-annotated data. More
importantly, along with face.evoLVe, images before & after alignment in the
common benchmark datasets are released with source codes and trained models
provided. All these efforts lower the technical burdens in reproducing the
existing methods for comparison, while users of our library could focus on
developing advanced approaches more efficiently. Last but not least,
face.evoLVe is well designed and vibrantly evolving, so that new face
recognition approaches can be easily plugged into our framework. Note that we
have used face.evoLVe to participate in a number of face recognition
competitions and secured the first place. The version that supports PyTorch is
publicly available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch and the
PaddlePaddle version is available at
https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/tree/master/paddle.
Face.evoLVe has been widely used for face analytics, receiving 2.4K stars and
622 forks.
