基于深度学习的图像识别与图像分割技术

    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
    
    # 定义卷积神经网络模型
    model = Sequential()
    model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(units=128, activation='relu'))
    model.add(Dense(units=10, activation='softmax'))
    
    # 编译模型
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    # 训练模型
    model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))
    
    # 评估模型
    score = model.evaluate(x_test, y_test)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

    import tensorflow as tf
    from tensorflow.keras.applications import ResNet50V2
    from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
    
    # 定义特征提取网络
    input_tensor = Input(shape=(224, 224, 3))
    base_model = ResNet50V2(input_tensor=input_tensor, weights='imagenet', include_top=False)
    
    # 定义区域提取网络
    x = base_model.output
    x = Conv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Flatten()(x)
    x = Dense(units=512, activation='relu')(x)
    rpn_cls = Dense(units=2, activation='softmax')(x)
    rpn_reg = Dense(units=4)(x)
    
    # 定义模型
    model = tf.keras.models.Model(inputs=base_model.input, outputs=[rpn_cls, rpn_reg])
    
    # 编译模型
    model.compile(optimizer='adam', loss=['categorical_crossentropy', 'mse'])
    
    # 训练模型
    model.fit(x_train, [y_train_cls, y_train_reg], batch_size=32, epochs=10, validation_data=(x_test, [y_test_cls, y_test_reg]))
    
    # 评估模型
    score = model.evaluate(x_test, [y_test_cls, y_test_reg])
    print('Test loss:', score[0])
    print('Test classification accuracy:', score[1])
    print('Test regression loss:', score[2])

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    # 定义FCN模型
    class FCN(nn.Module):
    def __init__(self):
        super(FCN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv5 = nn.Conv2d(512, 512, 3, padding=1)
        self.conv6 = nn.Conv2d(512, 4096, 7)
        self.conv7 = nn.Conv2d(4096, 4096, 1)
        self.score = nn.Conv2d(4096, 2, 1)
        self.upscore = nn.ConvTranspose2d(2, 2, 4, stride=2, bias=False)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2, stride=2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(F.relu(self.conv4(x)), 2, stride=2)
        x = F.relu(self.conv5(x))
        x = F.max_pool2d(F.relu(self.conv6(x)), 2, stride=2)
        x = F.relu(self.conv7(x))
        x = self.score(x)
        x = self.upscore(x)
        return x
    
    # 定义损失函数
    criterion = nn.CrossEntropyLoss()
    
    # 定义优化器
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    
    # 训练模型
    for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))
    
    # 评估模型
    correct = 0
    total = 0
    with torch.no_grad():
    for data in testloader:
        inputs, labels = data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

    import torch
    import torch.nn as nn
    import torchvision
    import torchvision.transforms as transforms
    
    # 定义图像分类模型
    class ImageClassifier(nn.Module):
    def __init__(self):
        super(ImageClassifier, self).__init__()
        self.layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.layer2 = nn.ReLU(inplace=True)
        self.layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.layer4 = nn.ReLU(inplace=True)
        self.layer5 = nn.Conv2d(in_channels=64, out_channels=10, kernel_size=1)
    
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        return x
    
    # 定义目标检测模型
    class ObjectDetector(nn.Module):
    def __init__(self):
        super(ObjectDetector, self).__init__()
        self.layer1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=1, padding=0)
        self.layer2 = nn.ReLU(inplace=True)
        self.layer3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=1, padding=0)
        self.layer4 = nn.ReLU(inplace=True)
        self.layer5 = nn.Conv2d(in_channels=128, out_channels=10, kernel_size=1, padding=0)
    
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        return x
    
    # 定义图像分类与目标检测模型集成
    class ImageClassifier_ObjectDetector(nn.Module):
    def __init__(self):
        super(ImageClassifier_ObjectDetector, self).__init__()
        self.classifier = ImageClassifier()
        self.detector = ObjectDetector()
    
    def forward(self, x):
        x = self.classifier(x)
        x = self.detector(x)
        return x
    
    # 加载数据集
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    
    # 加载数据集
    train_data = torchvision.datasets.ImageFolder('train', transform=transform)
    train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
    
    # 定义损失函数与优化器
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(x.device, lr=0.001)
    
    # 训练模型
    for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        images, labels = data
    
        # 前向传播
        outputs = self.forward(images)
        loss = criterion(outputs, labels)
    
        # 反向传播与优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
        running_loss += loss.item()
    
    print('Epoch {} loss: {}'.format(epoch+1, running_loss/len(train_loader)))
    
    # 测试模型
    correct = 0
    total = 0
    with torch.no_grad():
    for data in train_loader:
        images, labels = data
        outputs = self.forward(images)
        tensorboard_logs = []
        for i in range(images.size(0)):
            output = outputs[i]
            _, predicted = torch.max(output.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            tensorboard_logs.append('{} loss: {:.4f}'.format(i, output))
    
        print('Accuracy: {:.2f}%'.format(100*correct/total))
    
    # 运行模型
    correct = 0
    total = 0
    with torch.no_grad():
    for data in train_loader:
        images, labels = data
        outputs = self.forward(images)
        tensorboard_logs = []
        for i in range(images.size(0)):
            output = outputs[i]
            _, predicted = torch.max(output.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            tensorboard_logs.append('{} loss: {:.4f}'.format(i, output))
    
        print('Accuracy: {:.2f}%'.format(100*correct/total))