AI Source Detector (ViT-Base)
Detects and classifies the source of AI-generated images into five classes
(stable_diffusion, midjourney, dalle, real, other_ai).
Model Details
- Architecture: ViT-Base Patch-16 × 224
- Parameters: 86 M
- Fine-tuning epochs: 10
- Optimizer: AdamW (lr = 3e-5, wd = 0.01)
- Hardware: 1× NVIDIA RTX 4090 (24 GB)
Training Data
| Class | Images |
|---|---|
| Stable Diffusion | 12 000 |
| Midjourney | 10 500 |
| DALL-E 3 | 9 400 |
| Real | 11 800 |
| Other AI | 8 200 |
Total ≈ 52 k images - 80 % train / 10 % val / 10 % test.
Evaluation
| Metric | Top-1 | Macro F1 |
|---|---|---|
| Validation | 92.8 % | 0.928 |
| Test | 91.6 % | 0.914 |
Confusion Matrix (click to open)
Usage
from transformers import ViTImageProcessor, ViTForImageClassification, pipeline
classifier = pipeline(
task="image-classification",
model="yaya36095/ai-source-detector",
top_k=1
)
classifier("demo.jpg")
# → [{'label': 'stable_diffusion', 'score': 0.97}]
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