  In this paper, we contribute a new million-scale face benchmark containing
noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M
faces (WebFace42M) training data, as well as an elaborately designed
time-constrained evaluation protocol. Firstly, we collect 4M name list and
download 260M faces from the Internet. Then, a Cleaning Automatically utilizing
Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M,
which is efficient and scalable. To the best of our knowledge, the cleaned
WebFace42M is the largest public face recognition training set and we expect to
close the data gap between academia and industry. Referring to practical
scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol
and a test set are constructed to comprehensively evaluate face matchers.
  Equipped with this benchmark, we delve into million-scale face recognition
problems. A distributed framework is developed to train face recognition models
efficiently without tampering with the performance. Empowered by WebFace42M, we
reduce relative 40% failure rate on the challenging IJB-C set, and ranks the
3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior
performance compared with public training set. Furthermore, comprehensive
baselines are established on our rich-attribute test set under
FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet,
AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is
https://www.face-benchmark.org.
