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  ---
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  language: en
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  tags:
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- - image-classification
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- - ai-detection
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- - vit
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  license: mit
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  ---
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@@ -39,4 +39,44 @@ def detect_image(image_path):
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  confidence = predictions[0][prediction_id].item() * 100
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  result = "AI Generated" if prediction_id == 1 else "Real Image"
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- return result, confidence
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  language: en
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  tags:
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+ - image-classification
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+ - ai-detection
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+ - vit
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  license: mit
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  ---
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  confidence = predictions[0][prediction_id].item() * 100
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  result = "AI Generated" if prediction_id == 1 else "Real Image"
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+ return result, confidence
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+
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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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+
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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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+
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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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+
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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