从零开始搭建一个pytorch图像识别模型

从零开始搭建PyTorch图像识别模型通常涉及以下几个步骤[^1]：

1. **环境设置**:

   !pip install torch torchvision

2. **导入库和模块**:

   import torch
   from torchvision import datasets, transforms

3. **数据预处理**:

   transform = transforms.Compose([
       transforms.Resize(256),
       transforms.CenterCrop(224),
       transforms.ToTensor(),
       transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
   ])
   train_dataset = datasets.ImageFolder('path/to/train/dataset', transform=transform)

4. **创建数据加载器**:

   train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

5. **构建网络架构**:

   model = torchvision.models.resnet18(pretrained=True)  # 使用预训练的ResNet
   num_classes = len(train_dataset.classes)  # 获取类别数
   model.fc = nn.Linear(model.fc.in_features, num_classes)  # 修改分类层

6. **定义损失函数和优化器**:

   criterion = nn.CrossEntropyLoss()
   optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

7. **训练模型**:

   for epoch in range(num_epochs):
       for images, labels in train_loader:
           ...
           loss.backward()
           optimizer.step()
           optimizer.zero_grad()

8. **评估和保存模型**:

   model.eval()
   with torch.no_grad():
       predictions = model(images)
       _, predicted_labels = torch.max(predictions.data, 1)

完成以上步骤后，你就有了一个基础的PyTorch图像识别模型。请注意，实际操作时可能还需要调整超参数，如学习率、批大小和迭代次数（`num_epochs`）。