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README.md

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-license: mit
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+---
+license: mit
+---
+# AI Detection Model
+
+## Model Architecture and Training
+
+Three separate models were initially trained:
+1. Midjourney vs. Real Images
+2. Stable Diffusion vs. Real Images
+3. Stable Diffusion Fine-tunings vs. Real Images
+
+Data preparation process:
+- Used Google's Open Image Dataset for real images
+- Described real images using BLIP (Bootstrapping Language-Image Pre-training)
+- Generated Stable Diffusion images using BLIP descriptions
+- Found similar Midjourney images based on BLIP descriptions
+
+This approach ensured real and AI-generated images were as similar as possible, differing only in their origin.
+
+The three models were then distilled into a single EfficientNet model, combining their learned features for more efficient detection.
+
+## Data Sources
+
+- Google's Open Image Dataset: [link](https://storage.googleapis.com/openimages/web/index.html)
+- Ivan Sivkov's Midjourney Dataset: [link](https://www.kaggle.com/datasets/ivansivkovenin/midjourney-prompts-image-part8)
+- TANREI(NAMA)'s Stable Diffusion Prompts Dataset: [link](https://www.kaggle.com/datasets/tanreinama/900k-diffusion-prompts-dataset)
+
+## Performance
+
+- Validation Set: 94% accuracy
+  - Held out from training data to assess generalization
+
+- Custom Real-World Set: 84% accuracy
+  - Composed of self-captured images and online-sourced images
+  - Designed to be more representative of internet-based images
+
+- Comparative Analysis:
+  - Outperformed other popular AI detection models by 5 percentage points on both sets
+  - Other models achieved 89% and 79% on validation and real-world sets respectively
+
+## Key Insights
+
+1. Strong generalization on validation data (94% accuracy)
+2. Good adaptability to diverse, real-world images (84% accuracy)
+3. Consistent outperformance of other popular models
+4. 10-point accuracy drop from validation to real-world set indicates room for improvement
+5. Comprehensive training on multiple AI generation techniques contributes to model versatility
+6. Focus on subtle differences in image generation rather than content disparities
+
+## Future Directions
+
+- Expand dataset with more diverse, real-world examples to bridge the performance gap
+- Improve generalization to internet-sourced images
+- Conduct error analysis on misclassified samples to identify patterns
+- Integrate new AI image generation techniques as they emerge
+- Consider fine-tuning for specific domains where detection accuracy is critical