小麦病害数据集(resnet)
时间: 2023-12-07 18:03:16 浏览: 31
根据提供的引用内容,没有直接提到小麦病害数据集(resnet)。但是可以根据引用中提到的农作物叶子图像数据集,使用ResNet模型进行分类,其中包括小麦的病害分类。下面是使用ResNet模型对农作物叶子图像数据集进行分类的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.models import resnet18
# 定义数据预处理
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# 加载数据集
train_dataset = ImageFolder('path/to/train', transform=transform_train)
test_dataset = ImageFolder('path/to/test', transform=transform_test)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
# 定义模型
model = resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 61)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练模型
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.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 = model(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 test_loader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = model(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))
```
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