| --- |
| license: apache-2.0 |
| library_name: timm |
| pipeline_tag: image-classification |
| tags: |
| - image-classification |
| - ai-detection |
| - vit |
| datasets: |
| - your-username/ai-generated-vs-real |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # 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 | |
|
|
| <details> |
| <summary>Confusion Matrix (click to open)</summary> |
| <img src="confusion_matrix.png" width="480"> |
| </details> |
|
|
| ## Usage |
| ```python |
| 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}] |
| |
