Instructions to use Vishwajitm01/face-ai-detector-91auc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vishwajitm01/face-ai-detector-91auc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Vishwajitm01/face-ai-detector-91auc") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Vishwajitm01/face-ai-detector-91auc") model = AutoModelForImageClassification.from_pretrained("Vishwajitm01/face-ai-detector-91auc") - Notebooks
- Google Colab
- Kaggle
Face AI Detector (ViT Specialist)
This model is a fine-tuned version of the Vision Transformer (ViT) specialized in detecting AI-generated faces (StyleGAN/ThisPersonDoesNotExist).
Performance Summary
- AUC-ROC: 0.91
- Real-Face Accuracy: 91%
- AI-Face Accuracy: 81%
Project Notes
The model was trained using Sequential Transfer Learning:
- Initial training on CIFAKE for general AI artifacts detections.
- Specialist refinement on high-resolution facial datasets to identify StyleGAN-specific inconsistencies in skin texture and facial geometry.
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