验证码识别 深度学习 tensorflow 神经网络

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Deep Learning captcha_tf2
Overview

captcha_tf2

验证码识别 深度学习 tensorflow 神经网络
使用卷积神经网络，对字符，数字类型验证码进行识别，tensorflow使用2.0以上

目前项目还在更新中,诸多bug，欢迎提出issue和PR, 希望和你一起共同完善项目。

实例demo

训练过程

  • 优化器选择: Adam
  • 损失函数: MSLE(均方对数误差)
  • 数据集: 随机生成的10000张图片，按照8:2用于训练和验证
  • 设备: Titan X 在训练过程中第5次epoch即可达到 80的accuracy，50以上的val_accuracy。
    经过30次epoch accuracy达到93, val_acc在85以上
    最高达到97 acc

目前训练val提升可以，loss下降稳定


demo图片
效果

效果
效果

目录

1. 项目结构

1.1 文件目录

序号 文件 说明
1 model/ 模型权重文件
2 network/ 神经网络
3 settings_tf 项目配置文件
4 tools/ 工具文件
5 data/ 数据文件

1.2 主要文件

序号 文件 说明
1 train.py 训练程序
2 detect.py 测试程序
3 make_data.py 训练集合成程序
4 create_image.py 数据集生产脚本

2. 使用

修改主路径下derect.py的配置变量注:注意config.py的图片size
直接调用python detcet.py
保存格式:*.txt: [6, 9, 5, 6] 1.jpg

3. 训练

3.1 数据准备:

  • 如果自己收集数据较为繁琐，可直接调用create_image.py,修改相应配置即可快速生成图片集和标注文件 无需其他步骤
  • 或是自己去网络上寻找验证码图片集, 保存格式需以数字顺序保存，且标注文件存放在某个单独的 txt中，标注结果是对应的图片名数字-1作为下标 默认采用数据集样式为1.jpg, 2.jpg ...的顺序格式
|￣￣data/
|   |
|   |￣￣images/
|   |   |
|   |   |￣￣1.jpg
|   |   |
|   |   |￣￣2.jpg
|   |    
|   |￣￣label.txt

3.2开始训练

首先修改congig.py配置文件
接着修改train.py

  • 开始训练 python train.py
    训练中

网络

序号 层
输入 (B, 60, 160, 1)
1 卷积(32) relu BN
2 卷积(64) relu BN 相等池化
3 卷积(128) relu BN 相等池化
4 卷积(64) relu BN 相等池化
5 卷积(32) relu BN 相等池化
6 扁平化
8 全链接(onehot) softmax
输出 (长度, 类别)
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Comments
  • 验证码测试结果不正确？

    验证码测试结果不正确？

    [2, 21, 17, 35] 1.jpg [2, 31, 17, 35] 2.jpg [2, 21, 17, 35] 3.jpg [2, 31, 17, 35] 4.jpg [2, 31, 17, 35] 5.jpg [2, 21, 17, 35] 6.jpg [2, 31, 17, 35] 7.jpg [2, 21, 17, 35] 8.jpg [2, 31, 17, 35] 9.jpg [2, 21, 17, 35] 10.jpg 训练10组图片100轮 测试这10组图片结果验证码不正确

    opened by Reloss 3
Owner
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