• cosineface
• Contents
• CosFace Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
• • Script Parameters
  • Training Process
  • • Training
  • Evaluation Process
  • • Evaluation
• ModelZoo Homepage

cosineface

Contents

• CosFace Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script Parameters
  • Training Process
    • Training
  • Evaluation Process
    • Evaluation
• ModelZoo Homepage

CosFace Description

CosFace propose a novel loss function, namely large margin
cosine loss (LMCL), to realize this idea from a different
perspective. More specifically, we reformulate the softmax
loss as a cosine loss by L2 normalizing both features and
weight vectors to remove radial variations, based on which
a cosine margin term is introduced to further maximize the
decision margin in the angular space. As a result, minimum
intra-class variance and maximum inter-class variance are
achieved by virtue of normalization and cosine decision
margin maximization

Paper: Wang, Hao, et al. "Cosface: Large margin cosine loss for deep face recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

Model Architecture

We use backbone network of MobileFaceNet

Considering a scenario of binary-classes for example,
let θi denote the angle between the learned feature vector
and the weight vector of Class Ci (i = 1,2). The NSL
forces cos(θ1) > cos(θ2) for C1 , and similarly for C2,
so that features from different classes are correctly classified.
To develop a large margin classifier, we further require
cos(θ1) − m > cos(θ2) and cos(θ2) − m > cos(θ1), where
m ≥ 0 is a fixed parameter introduced to control the magnitude
of the cosine margin. Since cos(θi) − m is lower than
cos(θi), the constraint is more stringent for classification.
The above analysis can be well generalized to the scenario
of multi-classes. Therefore, the altered loss reinforces the
discrimination of learned features by encouraging an extra
margin in the cosine space.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Train Dataset used: Align-CASIA-WebFace
Test Dataset used: Align-LFW

• Train Dataset size：494414 images in 10575 classes
• Test Dataset size：6000 pairs of face images

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend.
• Framework
  • MindSpore 1.0.1
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

# enter script dir, train net
python train.py
# enter script dir, evaluate net
python eval_lfw.py

Script Description

Script Parameters

Major parameters in train.py and config.py as follows:

--batch_size: Training batch size.
--init_lr: the init Learning rate, default is 0.1.
--epoch_size: Epoch size, default is 70.
--lr_strategy: the lr strategy, default is preserve init lr, consine, Multistep.

We config the input image width 96, height 112
user must adjust the training image root in train.py line 70
user must adjust the weight file path in eval_lfw.py line 161 and test dataset root in eval_lfw.py line 163

Training Process

Training

• running on Ascend

  DEVICES_ID=0 nohup python train.py > log &
  

  After training, the loss value will be achieved as follows:

  epoch: 1 step: 1, loss is 29.115448
  epoch: 1 step: 2, loss is 29.015577
  epoch: 1 step: 3, loss is 30.393188
  epoch: 1 step: 4, loss is 29.896229
  epoch: 1 step: 5, loss is 31.129133
  epoch: 1 step: 6, loss is 31.261982
  epoch: 1 step: 7, loss is 31.596119
  epoch: 1 step: 8, loss is 31.839394
  epoch: 1 step: 9, loss is 32.113644
  epoch: 1 step: 10, loss is 32.441643
  ...
  

  The model checkpoint will be saved in the ./checkpoint directory.

Evaluation Process

Evaluation

Before running the command below, please check the checkpoint path used for evaluation.

• running on Ascend

  DEVICES_ID=0 python eval_lfw.py
  

  and can get accuracy
  'Accuracy': 98.82%

ModelZoo Homepage

Please check the official homepage.