身份证识别模型训练源码

身份证识别模型的训练源码可以基于深度学习框架进行开发，以下是一个基于PyTorch的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from myDataset import MyDataset

# 定义网络结构
class IDCardNet(nn.Module):
    def __init__(self):
        super(IDCardNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.relu3 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(64 * 6 * 16, 128)
        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)

        x = x.view(-1, 64 * 6 * 16)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# 数据预处理
data_transforms = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = MyDataset('./data/train/', data_transforms)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 开始训练
net = IDCardNet()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 保存模型
torch.save(net.state_dict(), './idcard_model.pth')

其中，`MyDataset`是一个自定义的`torch.utils.data.Dataset`类，用于加载训练数据集，需要根据实际情况进行编写。在上述代码中，我们定义了一个简单的卷积神经网络用于身份证识别，使用交叉熵损失和Adam优化器进行训练，并将训练好的模型保存在`./idcard_model.pth`文件中。