resnet实现猫狗分类

ResNet是一种深度卷积神经网络模型，其主要目的是解决深度网络训练中出现的梯度消失和梯度爆炸问题，使深度网络训练更加稳定和快速。而猫狗分类则是一种经典的图像识别任务，需要将输入的图像分为两类，即猫和狗。下面就是resnet实现猫狗分类的主要步骤：

1. 数据准备：从网络上下载猫和狗的图像数据集，并将其划分为训练集和验证集。

2. 数据增强：通过对图像进行旋转、缩放、平移等操作，增加训练数据的多样性，同时避免模型对训练数据的过度拟合。

3. 模型搭建：搭建ResNet模型，并将其连接到一个全连接层，最终输出猫和狗的分类结果。

4. 模型训练：使用训练集对模型进行训练，并在验证集上进行评估，调整模型参数以提高其性能。

5. 模型测试：使用测试集对模型进行验证，计算其准确率、精确率、召回率等指标，评估模型在猫狗分类任务上的性能。

总之，通过使用ResNet模型，进行数据准备、数据增强、模型搭建、模型训练和模型测试等步骤，可以实现对猫和狗进行准确的分类。这对于图像识别领域的研究和应用都具有重要意义。

相关问题

请帮我写一个基于ResNet18实现猫狗分类的程序

好的，以下是一个基于ResNet18实现猫狗分类的程序，使用PyTorch框架：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义数据预处理
transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载数据集
trainset = torchvision.datasets.ImageFolder(root='./train', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

testset = torchvision.datasets.ImageFolder(root='./test', transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2)

# 定义ResNet18模型
class ResNet18(nn.Module):
    def __init__(self, num_classes=2):
        super(ResNet18, self).__init__()
        self.resnet = torchvision.models.resnet18(pretrained=True)
        self.resnet.fc = nn.Linear(in_features=512, out_features=num_classes, bias=True)
    
    def forward(self, x):
        x = self.resnet(x)
        return x

# 定义模型、损失函数、优化器
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

net = ResNet18().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 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

print('Finished Training')

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        _, 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))

请注意，此程序假定您已经下载了猫狗数据集并将其分别放在train和test文件夹中。另外，此程序使用预训练的ResNet18模型，因此需要下载预训练权重。

使用cleanlab置信实现猫狗分类图像的错误标签的查找，用pytorch框架实现resnet8的分类

1. 使用cleanlab查找错误标签

cleanlab是一个Python库，可用于检测和纠正数据集中的错误标签。下面是使用cleanlab查找猫狗分类图像中的错误标签的示例代码：

import cleanlab
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score

# 加载数据集
train_dir = 'train/'
train_df = pd.read_csv('train.csv')
train_df['path'] = train_df['id'].map(lambda x: os.path.join(train_dir, '{}.jpg'.format(x)))
train_df['label'] = train_df['breed'].map(lambda x: class_to_idx[x])

# 定义模型和数据增强
model = torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(512, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

train_transforms = transforms.Compose([
    transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),
    transforms.RandomRotation(degrees=15),
    transforms.RandomHorizontalFlip(),
    transforms.CenterCrop(size=224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 获取训练集标签和预测概率
train_dataset = CustomDataset(train_df, train_transforms)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
train_labels = np.array(train_df['label'])
train_probs = cleanlab.util.label_model_predictions(model, train_loader)

# 查找错误标签
train_errors = cleanlab.pruning.get_noise_indices(
    s=train_labels,
    psx=train_probs,
    sorted_index_method='normalized_margin', # 使用标准化边界排序
    prune_method='prune_by_class', # 根据类别删除标签
    num_to_remove_per_class=2, # 每个类别删除两个标签
    seed=0
)

# 打印错误标签
print("Number of errors found:", len(train_errors))
print("Train accuracy:", accuracy_score(train_labels, np.argmax(train_probs, axis=1)))
print("Train accuracy after cleaning:", accuracy_score(train_labels[train_errors], np.argmax(train_probs[train_errors], axis=1)))

2. 使用pytorch实现resnet8分类模型

下面是使用pytorch实现resnet8分类模型的示例代码：

import torch.nn as nn

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet8(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet8, self).__init__()
        self.in_planes = 16

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
        self.linear = nn.Linear(64, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.avg_pool2d(out, 8)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

def ResNet8():
    return ResNet8(BasicBlock, [1, 1, 1], num_classes=2)