SST(Semi-Siamese Training)是一种针对浅层数据的人脸识别模型训练方法,所训练模型为一对半孪生网络,包括一个主模型和一个副模型,每次迭代时网络输入为同一ID的两张人脸图像(注册照和现场照),副模型从注册照中提取人脸特征并构成一个动态的特征队列,随着训练进行同步更新,根据主模型从现场照中提取的人脸特征和动态特征队列计算损失函数,得到损失值后主模型采用随机梯度下降的方式进行更新,副模型基于当前模型状态与主模型采用滑动平均的方式进行更新,训练完成后主模型用于人脸识别测试。
Text Pickle Python
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