if I want to use my image-text data to finetune the SigLIP2， where I can get the train code?
Author: LeopardCatCat
Pull request: no
Labels: name: Feature request

### Feature request

siglip2 finetune

### Motivation

siglip2 finetune

### Your contribution

no yet

Comments:
Hi! @LeopardCatCat 
If you want to fine-tune the model, here is a basic code structure:
```
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModel, AutoProcessor
from PIL import Image
import os

# Define a custom dataset class for image-text pairs
class ImageTextDataset(Dataset):
    def __init__(self, image_dir, text_file, processor):
        """
        Args:
            image_dir (str): Directory containing images
            text_file (str): Path to text file containing image descriptions
            processor: SigLIP2 processor for handling images and texts
        """
        self.image_dir = image_dir
        self.processor = processor
        self.image_text_pairs = []
        
        # Read text descriptions
        with open(text_file, 'r') as f:
            for line in f:
                image_name, description = line.strip().split('\t')
                self.image_text_pairs.append((image_name, description))
    
    def __len__(self):
        return len(self.image_text_pairs)
    
    def __getitem__(self, idx):
        image_name, description = self.image_text_pairs[idx]
        image_path = os.path.join(self.image_dir, image_name)
        
        # Load and process image
        image = Image.open(image_path).convert('RGB')
        
        # Process image and text using the processor
        inputs = self.processor(
            text=description,
            images=image,
            padding="max_length",  # Important: use max_length padding
            return_tensors="pt"
        )
        
        return inputs

def train_model(model, train_loader, num_epochs, learning_rate, device):
    """
    Train the SigLIP2 model
    
    Args:
        model: SigLIP2 model
        train_loader: DataLoader for training data
        num_epochs: Number of training epochs
        learning_rate: Learning rate for optimizer
        device: Device to train on (cuda or cpu)
    """
    # Set model to training mode
    model.train()
    
    # Define optimizer
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
    
    # Training loop
    for epoch in range(num_epochs):
        total_loss = 0
        for batch in train_loader:
            # Move batch to device
            batch = {k: v.to(device) for k, v in batch.items()}
            
            # Forward pass
            outputs = model(**batch, return_loss=True)
            loss = outputs.loss
            
            # Backward pass
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
            
            total_loss += loss.item()
        
        # Print epoch statistics
        avg_loss = total_loss / len(train_loader)
        print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}")

def main():
    # Set device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Load model and processor
    model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
    processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
    
    # Move model to device
    model = model.to(device)
    
    # Create dataset and dataloader
    dataset = ImageTextDataset(
        image_dir="path/to/your/images",
        text_file="path/to/your/text_descriptions.txt",
        processor=processor
    )
    
    train_loader = DataLoader(
        dataset,
        batch_size=32,
        shuffle=True,
        num_workers=4
    )
    
    # Train model
    train_model(
        model=model,
        train_loader=train_loader,
        num_epochs=10,
        learning_rate=1e-5,
        device=device
    )
    
    # Save the fine-tuned model
    model.save_pretrained("path/to/save/fine_tuned_model")

if __name__ == "__main__":
    main()
```
Feel free to try it out :)

@ailunc thanks a lot！！！I am a sutdent , I am trying to read these code，thank you very much!