更新 README.md

README.md

@@ -21,144 +21,7 @@ The model is trained on a dataset of cat and dog images. The training process in
 
 Here is a simplified version of the training code:
 
-```python
-import os
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, Dataset
-from torchvision import models, transforms
-from PIL import Image
-from safetensors.torch import save_file
-
-class CatDogDataset(Dataset):
-    def __init__(self, root_dir, transform=None):
-        self.root_dir = root_dir
-        self.transform = transform
-        self.image_paths = []
-        self.labels = []
-
-        for filename in os.listdir(root_dir):
-            if 'cat' in filename:
-                self.image_paths.append(os.path.join(root_dir, filename))
-                self.labels.append(0)  # cat
-            elif 'dog' in filename:
-                self.image_paths.append(os.path.join(root_dir, filename))
-                self.labels.append(1)  # dog
-
-    def __len__(self):
-        return len(self.image_paths)
-
-    def __getitem__(self, idx):
-        img_path = self.image_paths[idx]
-        image = Image.open(img_path).convert('RGB')
-        label = self.labels[idx]
-
-        if self.transform:
-            image = self.transform(image)
-
-        return image, label
-
-# Data preprocessing
-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])
-    ]),
-}
-
-data_dir = 'dog-cat'
-image_datasets = {x: CatDogDataset(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
-dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4) for x in ['train', 'val']}
-dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
-class_names = ['cat', 'dog']
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load and modify ResNet-50
-model_ft = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
-num_ftrs = model_ft.fc.in_features
-model_ft.fc = nn.Linear(num_ftrs, len(class_names))
-
-model_ft = model_ft.to(device)
-
-criterion = nn.CrossEntropyLoss()
-
-# Optimizer
-optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
-
-# Learning rate scheduler
-exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
-
-# Training function
-def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
-    best_model_wts = model.state_dict()
-    best_acc = 0.0
-
-    for epoch in range(num_epochs):
-        print(f'Epoch {epoch}/{num_epochs - 1}')
-        print('-' * 10)
-
-        for phase in ['train', 'val']:
-            if phase == 'train':
-                model.train()
-            else:
-                model.eval()
-
-            running_loss = 0.0
-            running_corrects = 0
-
-            for inputs, labels in dataloaders[phase]:
-                inputs = inputs.to(device)
-                labels = labels.to(device)
-
-                optimizer.zero_grad()
-
-                with torch.set_grad_enabled(phase == 'train'):
-                    outputs = model(inputs)
-                    _, preds = torch.max(outputs, 1)
-                    loss = criterion(outputs, labels)
-
-                    if phase == 'train':
-                        loss.backward()
-                        optimizer.step()
-
-                running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
-
-            if phase == 'train':
-                scheduler.step()
-
-            epoch_loss = running_loss / dataset_sizes[phase]
-            epoch_acc = running_corrects.double() / dataset_sizes[phase]
-
-            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
-
-            if phase == 'val' and epoch_acc > best_acc:
-                best_acc = epoch_acc
-                best_model_wts = model.state_dict()
-
-        print()
-
-    print(f'Best val Acc: {best_acc:4f}')
-    model.load_state_dict(best_model_wts)
-    return model
-
-# Train and evaluate the model
-model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
-
-# Save the model
-torch.save(model_ft.state_dict(), 'model_cat_dog_classifier.pt')
-save_file(model_ft.state_dict(), 'model_cat_dog_classifier.safetensors')
-```
+[train.py](./train.py)
 
 ## Inference