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README.md
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This model is designed to detect whether an image is real or AI-generated. It uses Vision Transformer (VIT) architecture to provide accurate classification.
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## Model Usage
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```python
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from transformers import ViTImageProcessor, ViTForImageClassification
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from PIL import Image
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# Example usage
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# result, confidence = detect_image("path/to/image.jpg")
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# print(f"Result: {result} (Confidence: {confidence:.2f}%)")
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Classes
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The model classifies images into two categories:
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Real Image (0)
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AI Generated (1)
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Technical Details
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Model Architecture: Vision Transformer (ViT)
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Input: Images (RGB)
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Output: Binary classification with confidence score
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Max Image Size: 224x224 (automatically resized)
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Requirements
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transformers>=4.30.0
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torch>=2.0.0
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Pillow>=9.0.0
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Limitations
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Best performance with clear, high-quality images
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May have reduced accuracy with heavily edited photos
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Designed for general image detection
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Web Integration Example
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javascript
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CopyInsert
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async function detectImage(imageFile) {
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const formData = new FormData();
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formData.append('image', imageFile);
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const response = await fetch('YOUR_API_ENDPOINT', {
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method: 'POST',
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body: formData
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});
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return await response.json();
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}
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Developer
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Created by: yaya36095
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License: MIT
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Repository: https://huggingface.co/yaya36095/ai-image-detector
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This model is designed to detect whether an image is real or AI-generated. It uses Vision Transformer (VIT) architecture to provide accurate classification.
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## Model Usage
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```python
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from transformers import ViTImageProcessor, ViTForImageClassification
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from PIL import Image
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# Example usage
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# result, confidence = detect_image("path/to/image.jpg")
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# print(f"Result: {result} (Confidence: {confidence:.2f}%)")
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