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验证码总共3种类型,字母数字、计算题和中文;
三种类型的字符长度不同,但验证码宽高相同;
如果分别训练,那会生成3种模型文件,部署识别时,不能准确选择模型文件,导致识别错误;
训练时,Loss Function选择的是CrossEntropy,指定label num,针对三种类型分别训练;
如果选择CTC,好像会忽略label num,但是发现需要的样本数量比CrossEntropy多很多;
请问这种多类型的验证码需要如何训练和部署?
The text was updated successfully, but these errors were encountered:
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验证码总共3种类型,字母数字、计算题和中文;
三种类型的字符长度不同,但验证码宽高相同;
如果分别训练,那会生成3种模型文件,部署识别时,不能准确选择模型文件,导致识别错误;
训练时,Loss Function选择的是CrossEntropy,指定label num,针对三种类型分别训练;
如果选择CTC,好像会忽略label num,但是发现需要的样本数量比CrossEntropy多很多;
请问这种多类型的验证码需要如何训练和部署?
The text was updated successfully, but these errors were encountered: