pytorch花卉识别

### 使用 PyTorch 实现花卉图像分类 #### 数据准备与加载 为了构建一个有效的花卉图像分类器，首先需要准备好数据集并对其进行适当处理。这里采用的是Flowers-102数据集[^1]。通过`torchvision.datasets.ImageFolder`来加载图片，并利用`transforms.Compose`定义一系列的数据转换操作。 ```python from torchvision import datasets, transforms import torch.utils.data as data data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } image_datasets = {x: datasets.ImageFolder(root=f'dataset/{x}', transform=data_transforms[x]) for x in ['train', 'val']} dataloaders = {x: data.DataLoader(image_datasets[x], batch_size=32, shuffle=True) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes ``` #### 构建模型 接下来是创建用于分类的神经网络模型。此处选择了经典的残差网络——ResNet作为基础框架。可以通过修改预训练权重与否来自定义初始化方式。 ```python import torchvision.models as models model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, len(class_names)) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_ft = model_ft.to(device) criterion = nn.CrossEntropyLoss() optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9) exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) ``` #### 训练过程 设置好损失函数、优化算法之后就可以进入正式的训练环节了。对于每一轮迭代，在训练模式下更新参数；而在验证阶段则评估当前模型性能。 ```python def train_model(model, criterion, optimizer, scheduler, num_epochs=25): best_acc = 0.0 for epoch in range(num_epochs): print(f'Epoch {epoch}/{num_epochs - 1}') # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': model.train(True) else: model.eval() running_loss = 0.0 running_corrects = 0 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) if phase == 'train': optimizer.zero_grad() loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc scheduler.step() return model ``` 完成上述步骤后，调用`train_model()`函数启动整个流程即可得到经过充分训练后的花卉识别模型。 #### 测试与预测 当模型已经过良好训练后，便可以将其应用于新的未知样本上进行预测。除了基本的功能外，还提供了TTA(Test Time Augmentation)[^2] 和 CAM(Class Activation Mapping)[^2] 功能以提高准确性并解释决策依据。 最后提醒一点关于开发环境的选择建议使用Anaconda管理依赖关系，并搭配PyCharm或VSCode这类IDE来进行编码工作[^3]。