Code of TIP2021 Paper《SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition》. We provide both MxNet and Pytorch versions.

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Deep Learning SFace
Overview

SFace

Code of TIP2021 Paper 《SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition》.

We provide both MxNet, PyTorch and Jittor versions.

Abstract

Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the inter-class distance. However, they may neglect that there are also low quality training images which should not be optimized in this strict way. Considering the imperfection of training databases, we propose that intra-class and inter-class objectives can be optimized in a moderate way to mitigate overfitting problem, and further propose a novel loss function, named sigmoid-constrained hypersphere loss (SFace). Specifically, SFace imposes intra-class and inter-class constraints on a hypersphere manifold, which are controlled by two sigmoid gradient re-scale functions respectively. The sigmoid curves precisely re-scale the intra-class and inter-class gradients so that training samples can be optimized to some degree. Therefore, SFace can make a better balance between decreasing the intra-class distances for clean examples and preventing overfitting to the label noise, and contributes more robust deep face recognition models. Extensive experiments of models trained on CASIA-WebFace, VGGFace2, and MS-Celeb-1M databases, and evaluated on several face recognition benchmarks, such as LFW, MegaFace and IJB-C databases, have demonstrated the superiority of SFace.

arch

Usage

Please check your familiar version: MxNet and PyTorch, PyTorch and Jittor versions.

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Comments
  • pixel scaling

    pixel scaling

    https://github.com/zhongyy/SFace/blob/cb5ffb3c4863eecb3abf6a80529daffb05a0e820/SFace_torch/backbone/model_mobilefacenet.py#L81

    According to the paper, this should be 0,0078125 ?

    opened by rgrbic 1
  • intraloss和interloss是否总是分别在减小和增大？

    intraloss和interloss是否总是分别在减小和增大？

    我最近在声音的一些识别工作上尝试使用SFace，发现在第一个epoch中的最初intraloss是在不停减少的，但是到后面的epoch甚至会逐渐增大，稳定在一个范围，但是错误率在下降，这是比较反优化目标，或者说这样的稳定就是把loss控制在一个合适的范围？ 这是我的训练loss，我使用的是adam+Cyclical Learning Rates学习率调整策略（学习率会周期从最低到最高），这和您在论文中使用的SGD+固定缩减lr不太相同。 epoch: 1, lr: 7.27e-03 - train loss: -6.43e+00, train intra_loss: -2.75e+01, train inter_loss: 21.07 - valid loss: -1.13e+01, valid intra_loss: -3.15e+01, valid inter_loss: 20.16, valid ErrorRate: 5.17e-02 epoch: 2, lr: 5.47e-03 - train loss: -1.18e+01, train intra_loss: -2.86e+01, train inter_loss: 16.84 - valid loss: -1.21e+01, valid intra_loss: -2.97e+01, valid inter_loss: 17.54, valid ErrorRate: 3.19e-02 epoch: 3, lr: 1.80e-03 - train loss: -1.23e+01, train intra_loss: -2.86e+01, train inter_loss: 16.27 - valid loss: -1.09e+01, valid intra_loss: -2.58e+01, valid inter_loss: 14.86, valid ErrorRate: 2.51e-02 epoch: 4, lr: 9.07e-03 - train loss: -1.16e+01, train intra_loss: -2.53e+01, train inter_loss: 13.62 - valid loss: -1.59e+01, valid intra_loss: -3.04e+01, valid inter_loss: 14.43, valid ErrorRate: 3.47e-02 epoch: 5, lr: 3.67e-03 - train loss: -1.07e+01, train intra_loss: -2.33e+01, train inter_loss: 12.57 - valid loss: -1.01e+01, valid intra_loss: -2.24e+01, valid inter_loss: 12.34, valid ErrorRate: 2.12e-02 epoch: 6, lr: 3.60e-03 - train loss: -1.18e+01, train intra_loss: -2.48e+01, train inter_loss: 13.01 - valid loss: -1.16e+01, valid intra_loss: -2.46e+01, valid inter_loss: 13.06, valid ErrorRate: 2.38e-02 epoch: 7, lr: 9.14e-03 - train loss: -1.00e+01, train intra_loss: -2.07e+01, train inter_loss: 10.69 - valid loss: -1.65e+01, valid intra_loss: -2.86e+01, valid inter_loss: 12.16, valid ErrorRate: 2.80e-02 epoch: 8, lr: 1.88e-03 - train loss: -1.03e+01, train intra_loss: -2.16e+01, train inter_loss: 11.32 - valid loss: -7.19e+00, valid intra_loss: -1.79e+01, valid inter_loss: 10.75, valid ErrorRate: 1.86e-02 epoch: 9, lr: 5.39e-03 - train loss: -1.10e+01, train intra_loss: -2.21e+01, train inter_loss: 11.10 - valid loss: -1.31e+01, valid intra_loss: -2.48e+01, valid inter_loss: 11.78, valid ErrorRate: 2.52e-02 epoch: 10, lr: 7.35e-03 - train loss: -8.92e+00, train intra_loss: -1.82e+01, train inter_loss: 9.27 - valid loss: -1.56e+01, valid intra_loss: -2.55e+01, valid inter_loss: 9.91, valid ErrorRate: 2.32e-02

    opened by zhaoyiming 1
  • Initialization method of weights

    Initialization method of weights

    Hi, yy, I was wondering why you choose xavier_normal_(self.weight, gain=2, mode='out') instead of nn.init.xavier_uniform_(self.weight) when initializing weights. And by looking through the xavier_normal_ function, I found the weights won't participate in gradient propagation, why?

    Thanks for your sharing, and have a nice day :)

    opened by CloudWalking0 0
Owner
Zhong Yaoyao
PhD student in BUPT
Zhong Yaoyao
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