WebFace260M
Introduced by Zhu et al. in WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
WebFace260M is a million-scale face benchmark, which is constructed for the research community towards closing the data gap behind the industry.
It consists of: - Noisy 4M identities and 260M faces - High-quality training data with 42M images of 2M identities by using automatic cleaning - A test set with rich attributes and a time-constrained evaluation protocol
Papers
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