vit-base-patch16-224-finetuned-forgery

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vit-base-patch16-224-finetuned-forgery

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0659
• Accuracy: 0.9762

Model description

More information needed

Intended uses & limitations

To use, combine known genuine signature with questioned signature into a single image, then run inference.

from PIL import Image

def create_comparison_image(img1_path, img2_path):
    # Open images
    img1 = Image.open(img1_path).convert("RGB")
    img2 = Image.open(img2_path).convert("RGB")
    
    # Resize to same height
    height = max(img1.height, img2.height)
    width1 = int(img1.width * (height / img1.height))
    width2 = int(img2.width * (height / img2.height))
    
    img1 = img1.resize((width1, height), Image.LANCZOS)
    img2 = img2.resize((width2, height), Image.LANCZOS)
    
    # Create new image with space for both images
    total_width = width1 + width2
    comparison = Image.new('RGB', (total_width, height))
    
    # Paste images side by side
    comparison.paste(img1, (0, 0))
    comparison.paste(img2, (width1, 0))
    
    return comparison

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2.1687073595562957e-05
• train_batch_size: 29
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 5
• total_train_batch_size: 145
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1
• num_epochs: 5

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1