Training Mode。传统的人脸识别训练模块作为基线训练。具体地,通过DataLoader调度训练输入,然后将输入发送到主干网络进行学习,最后计算一个准则作为向后更新的训练损失。此外,还考虑了人脸识别的实际情况,即用浅层分布式数据训练网络。因此,整合了最近的训练策略,以促进对浅层人脸数据的训练。
AM-Softmax: An additive margin loss that adds a cosine margin penalty to the target logit.
ArcFace: An additive angular margin loss that adds a margin penalty to the target angle.
AdaCos: A cosine-based softmax loss that is hyperparameter-free and adaptive scaling.
AdaM-Softmax: An adaptive margin loss that can adjust the margins for different classes adaptively.
CircleLoss: A unified formula that learns with class-level labels and pair-wise labels.
CurricularFace: An loss function that adaptively adjusts the importance of easy and hard samples during different training stages.
MV-Softmax: A loss function that adaptively em- phasizes the mis-classified feature vectors to guide the discriminative feature learning.
NPCFace:A loss function that emphasizes the training on both the negative and positive hard cases
Test Protocol。有各种基准来测量人脸识别模型的准确性。他们中的许多人关注特定的人脸识别挑战,比如cross age, cross pose, and cross race。其中,常用的测试主要基于LFW和MegaFace。将这些协议与简单的使用和清晰的指令集成到FaceX-Zoo中,人们可以通过简单的配置在单个或多个基准测试上轻松地测试他们的模型。此外,通过添加测试数据和分析测试对,可以方便地扩展额外的测试协议。值得注意的是,还提供了一个基于MegaFace的蒙面人脸识别基准。
LFW: It contains 13,233 web-collected images of 5,749 identities with the pose, expression and illu- mination variations. We report the mean accuracy of 10-fold cross validation on this classic benchmark.
CPLFW: It contains 11,652 images of 3,930 iden- tities, which focuses on cross-pose face verification. Following the official protocol, the mean accuracy of 10-fold cross validation is adopted.
CALFW: It contains 12,174 images of 4,025 identities, aiming at cross-age face verification. The mean accuracy of 10-fold cross validation is adopted.
AgeDB30: It contains 12,240 images of 440 iden- tities, where each test pair has an age gap of 30 years. We report the mean accuracy of 10-fold cross valida- tion.
RFW: It contains 40,607 images of 11,430 identi- ties, which is proposed to measure the potential racial bias in face recognition. There are four test subsets in RFW, named African, Asian, Caucasian and Indian, and we report the mean accuracy of each subset, re- spectively.
MegaFace: It contains 80 probe identities with 1 million gallery distractors, aiming at evaluating large- scale face recognition performance. We report the Rank-K identification accuracy on MegaFace.
MegaFace-Mask: It contains the same probe identities and gallery distractors with MegaFace, while each probe image is added by a virtual mask. This protocol is designed to evaluate large-scale masked face recog- nition performance.