cifar100图像分类pytorch详细步骤

首先，你需要下载 CIFAR-100 数据集并将其解压缩。然后，你需要使用 PyTorch 加载数据集并进行预处理。以下是详细步骤：

1. 导入必要的库：

import torch
import torchvision
import torchvision.transforms as transforms

2. 定义数据预处理：

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

3. 加载训练集和测试集：

trainset = torchvision.datasets.CIFAR100(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR100(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

4. 定义类别标签：

classes = ('beaver', 'dolphin', 'otter', 'seal', 'whale',
           'aquarium fish', 'flatfish', 'ray', 'shark', 'trout',
           'orchids', 'poppies', 'roses', 'sunflowers', 'tulips',
           'bottles', 'bowls', 'cans', 'cups', 'plates',
           'apples', 'mushrooms', 'oranges', 'pears', 'sweet peppers',
           'clock', 'computer keyboard', 'lamp', 'telephone', 'television',
           'bed', 'chair', 'couch', 'table', 'wardrobe',
           'bee', 'beetle', 'butterfly', 'caterpillar', 'cockroach',
           'bear', 'leopard', 'lion', 'tiger', 'wolf',
           'bridge', 'castle', 'house', 'road', 'skyscraper',
           'cloud', 'forest', 'mountain', 'plain', 'sea',
           'camel', 'cattle', 'chimpanzee', 'elephant', 'kangaroo',
           'fox', 'porcupine', 'possum', 'raccoon', 'skunk',
           'crab', 'lobster', 'snail', 'spider', 'worm',
           'baby', 'boy', 'girl', 'man', 'woman',
           'crocodile', 'dinosaur', 'lizard', 'snake', 'turtle',
           'hamster', 'mouse', 'rabbit', 'shrew', 'squirrel',
           'maple', 'oak', 'palm', 'pine', 'willow',
           'bicycle', 'bus', 'motorcycle', 'pickup truck', 'train',
           'lawn-mower', 'rocket', 'streetcar', 'tank', 'tractor')

5. 定义神经网络模型：

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 100)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

6. 定义损失函数和优化器：

import torch.optim as optim

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

7. 训练模型：

for epoch in range(2):  # 多次循环遍历数据集

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 获取输入数据
        inputs, labels = data

        # 梯度清零
        optimizer.zero_grad()

        # 正向传播、反向传播、优化
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 打印统计信息
        running_loss += loss.item()
        if i % 2000 == 1999:    # 每 2000 个小批量数据打印一次
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

8. 测试模型：

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        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 10000 test images: %d %%' % (
    100 * correct / total))

这就是 CIFAR-100 图像分类的 PyTorch 实现步骤。