train_model.py

import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, random_split
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision.datasets import ImageFolder
import os

def main():
    dataset_path = "categorized_images"
    if not os.path.exists(dataset_path):
        raise FileNotFoundError(f"❌ Dataset folder '{dataset_path}' not found!")

    # Get class names dynamically from dataset folders
    class_names = sorted(os.listdir(dataset_path))
    num_classes = len(class_names)

    # Data Augmentation & Normalization
    train_transform = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    val_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])
    ])

    dataset = ImageFolder(root=dataset_path, transform=train_transform)
    train_size = int(0.8 * len(dataset))
    val_size = len(dataset) - train_size
    train_dataset, val_dataset = random_split(dataset, [train_size, val_size])

    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4, pin_memory=True)
    val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False, num_workers=4, pin_memory=True)

    # Load Pretrained Model
    model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.IMAGENET1K_V1)
    
    # Freeze all layers except the classifier
    for param in model.features.parameters():
        param.requires_grad = False

    # Update the classifier for our dataset
    model.classifier[1] = nn.Linear(1280, num_classes)
    
    # Unfreeze last 3 layers to fine-tune
    for param in model.features[-3:].parameters():
        param.requires_grad = True

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.0001)
    scheduler = ReduceLROnPlateau(optimizer, 'min', patience=3, factor=0.1)

    best_val_loss = float('inf')
    for epoch in range(30):
        model.train()
        train_loss = 0.0
        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
        avg_train_loss = train_loss / len(train_loader)

        model.eval()
        val_loss, correct, total = 0.0, 0, 0
        with torch.no_grad():
            for images, labels in val_loader:
                images, labels = images.to(device), labels.to(device)
                outputs = model(images)
                loss = criterion(outputs, labels)
                val_loss += loss.item()
                _, predicted = torch.max(outputs, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()
        avg_val_loss = val_loss / len(val_loader)
        val_accuracy = 100 * correct / total

        print(f"📢 Epoch [{epoch+1}/30] → Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Accuracy: {val_accuracy:.2f}%")
        scheduler.step(avg_val_loss)

        if avg_val_loss < best_val_loss:
            best_val_loss = avg_val_loss
            torch.save(model.state_dict(), "custom_image_model.pth")
            print("✅ Best model saved!")

    print("🎉 Training Complete!")

if __name__ == '__main__':
    main()