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README.md

@@ -28,12 +28,6 @@ model-index:
     - type: f1
       value: 0.9960
       name: F1 Score
-    - type: precision
-      value: 0.9930
-      name: Precision
-    - type: recall
-      value: 0.9990
-      name: Recall
 ---
 
 # SDXL Detector - Vision Transformer
@@ -50,83 +44,20 @@ This model is a **specialized binary classifier** trained to detect images gener
 - 🛡️ **Robust**: Trained with 6-layer overfitting prevention
 - 📊 **Well-Validated**: Separate train/val/test splits with no overlap
 
-### Model Details
-
-- **Base Model**: google/vit-base-patch16-224 (Vision Transformer)
-- **Task**: Binary Image Classification (Real vs SDXL-Fake)
-- **Input**: 224×224 RGB images
-- **Output**: 2 classes (0: Real, 1: SDXL-Fake)
-- **Parameters**: 85.8M total
-
-## Performance
-
-### Test Set Results
+### Performance
 
 ```
-Accuracy:  0.9960
-Precision: 0.9930
-Recall:    0.9990
-F1 Score:  0.9960
-AUC-ROC:   0.9999
+Test Accuracy:  0.9960
+Precision:      0.9930
+Recall:         0.9990
+F1 Score:       0.9960
+AUC-ROC:        0.9999
 
 False Positive Rate: 0.0070
 False Negative Rate: 0.0010
 ```
 
-### Confusion Matrix
-
-```
-                Predicted
-              Real    Fake
-Actual Real    993       7
-Actual Fake      1     999
-```
-
-**Interpretation:**
-- Out of 1,000 real images: 993 correctly identified (99.3%)
-- Out of 1,000 SDXL images: 999 correctly identified (99.9%)
-
-## Training Details
-
-### Dataset
-
-**Training Data:**
-- Real Images: 8,000 (WikiArt paintings)
-- SDXL Images: 8,000 (generated with SDXL base model)
-- Total: 16,000 images
-
-**Validation & Test:**
-- 2,000 images each (1,000 real + 1,000 SDXL)
-- Completely separate from training data
-
-### Training Configuration
-
-```python
-Model: Vision Transformer (ViT-base-patch16-224)
-Optimizer: AdamW
-Learning Rate: 2e-5
-Batch Size: 32
-Epochs: 3 (early stopping from max 20)
-Training Time: 21.7 minutes
-
-Overfitting Prevention:
-- Early Stopping (patience=5)
-- Data Augmentation (random crops, flips, rotations, color jitter)
-- Dropout (0.1)
-- Label Smoothing (0.1)
-- Weight Decay (0.01)
-- Learning Rate Scheduling
-```
-
-## Usage
-
-### Installation
-
-```bash
-pip install transformers torch pillow
-```
-
-### Quick Start
+## Quick Start
 
 ```python
 import torch
@@ -141,99 +72,41 @@ processor = ViTImageProcessor.from_pretrained(
     "google/vit-base-patch16-224"
 )
 
-# Load and preprocess image
-image = Image.open("your_image.jpg")
+# Load image
+image = Image.open("test.jpg")
 inputs = processor(images=image, return_tensors="pt")
 
 # Get prediction
 model.eval()
 with torch.no_grad():
     outputs = model(**inputs)
-    logits = outputs.logits
-    probs = torch.softmax(logits, dim=1)
-    prediction = logits.argmax(dim=1).item()
-
-# Interpret results
-if prediction == 1:
-    confidence = probs[0][1].item()
-    print(f"SDXL-Generated (confidence: {confidence:.2%})")
-else:
-    confidence = probs[0][0].item()
-    print(f"Real Image (confidence: {confidence:.2%})")
+    probs = torch.softmax(outputs.logits, dim=1)
+    
+    if probs[0][1] > 0.5:
+        print(f"SDXL-Generated ({probs[0][1]:.2%} confident)")
+    else:
+        print(f"Real Image ({probs[0][0]:.2%} confident)")
 ```
 
-### Advanced Usage with Threshold
+## Using the model.py Helper
 
 ```python
-def detect_sdxl(image_path, threshold=0.5):
-    """
-    Detect if image is SDXL-generated
-    
-    Args:
-        image_path: Path to image
-        threshold: Classification threshold (default 0.5)
-        
-    Returns:
-        dict: {is_sdxl: bool, confidence: float, label: str}
-    """
-    image = Image.open(image_path).convert('RGB')
-    inputs = processor(images=image, return_tensors="pt")
-    
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.softmax(outputs.logits, dim=1)
-        sdxl_prob = probs[0][1].item()
-    
-    is_sdxl = sdxl_prob > threshold
-    
-    return {
-        'is_sdxl': is_sdxl,
-        'confidence': sdxl_prob if is_sdxl else (1 - sdxl_prob),
-        'label': 'SDXL-Generated' if is_sdxl else 'Real Image',
-        'sdxl_probability': sdxl_prob,
-        'real_probability': 1 - sdxl_prob
-    }
+from model import detect_image
 
-# Example
-result = detect_sdxl("test_image.jpg")
-print(f"{result['label']} ({result['confidence']:.2%} confident)")
+result = detect_image("test.jpg", model_path="ash12321/sdxl-detector-vit")
+print(f"Is Fake: {result['is_fake']}")
+print(f"Confidence: {result['confidence']:.2%}")
 ```
 
-## Limitations
-
-### What This Model Detects
-
-✅ **SDXL-generated images** (Stable Diffusion XL)
-
-### What This Model Does NOT Detect
-
-❌ Other AI generators (FLUX, Midjourney, DALL-E, etc.)
-❌ Edited/manipulated real images
-❌ Heavily compressed or low-quality images may reduce accuracy
-
-**Recommendation**: Use as part of an ensemble with other specialized detectors for comprehensive AI detection.
+## Files in this Repository
 
-## Intended Use
-
-### Primary Use Cases
-
-- Content moderation platforms
-- Academic research on AI-generated content
-- Watermarking and provenance systems
-- Educational tools for AI literacy
-
-### Out-of-Scope Uses
-
-- Sole basis for legal decisions
-- Detection of non-SDXL generators without validation
-- Processing of illegal or harmful content
-
-## Ethical Considerations
-
-- This model should be used responsibly as part of broader content verification systems
-- Performance may degrade on images outside the training distribution
-- Always combine automated detection with human review for critical decisions
-- Be transparent about using AI detection systems
+- `pytorch_model.bin` - Model weights
+- `config.json` - Model configuration
+- `model.py` - Model architecture and helper functions
+- `README.md` - This documentation
+- `training_results.json` - Detailed training metrics
+- `training_curves.png` - Training visualization
+- `confusion_matrix.png` - Test set confusion matrix
 
 ## Citation
 
@@ -247,16 +120,7 @@ print(f"{result['label']} ({result['confidence']:.2%} confident)")
 }
 ```
 
-## Model Card Authors
-
-ash12321
-
-## Model Card Contact
-
-For questions or feedback, please open an issue on the model repository.
-
 ---
 
+**License**: Apache 2.0  
 **Created**: 2025-12-31
-**Framework**: PyTorch + Transformers
-**License**: Apache 2.0