---
license: apache-2.0
labels:
  "LABEL_0": FAKE
  "LABEL_1": REAL
---

This is a simple AI image detection model utilizing visual transformers trained on the CIFake dataset.

Example usage:
```python
import torch
from PIL import Image
from torchvision import transforms
from transformers import ViTForImageClassification, ViTImageProcessor

# Load the trained model
model_path = 'vit_model.pth'
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
model.classifier = torch.nn.Linear(model.classifier.in_features, 2)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()

# Define the image preprocessing pipeline
preprocess = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

def predict(image_path, model, preprocess):
    # Load and preprocess the image
    image = Image.open(image_path).convert('RGB')
    inputs = preprocess(image).unsqueeze(0)

    # Perform inference
    with torch.no_grad():
        outputs = model(inputs).logits
        predicted_label = torch.argmax(outputs).item()

    # Map the predicted label to the corresponding class
    label_map = {0: 'FAKE', 1: 'REAL'}
    predicted_class = label_map[predicted_label]
    return predicted_class

# Example usage
image_paths = [
    'path/to/image.jpg',
    'path/to/image.jpg',
    'path/to/image.jpg'
]

for image_path in image_paths:
    predicted_class = predict(image_path, model, preprocess)
    print(f'Predicted class: {predicted_class}', image_path)
```