Deepfake Image Detector (CILab Fine-Tuned)
Model Performance
- Test Accuracy: 73.17%
- Best Validation Accuracy: 86.27%
- Best Epoch: 11
- Planned Epochs: 12
- Actual Epochs Trained: 12 (early stopping applied)
Dataset
- Training: 1,632 images
- Validation: 204 images
- Test: 205 images
Training Details
Training was stopped early at epoch 12 due to early stopping criteria being met. The best model was achieved at epoch 11 with validation accuracy of 86.27%.
Usage
from transformers import ViTForImageClassification, ViTFeatureExtractor
model = ViTForImageClassification.from_pretrained('shivani1511/deepfake-image-detector-new-latest')
feature_extractor = ViTFeatureExtractor.from_pretrained('shivani1511/deepfake-image-detector-new-latest')
Notes
- Fine-tuned on CILab dataset to address AI-generated fake detection (e.g., StyleGAN).
- Base model: shivani1511/deepfake-image-detector-new (Vision Transformer).
- Improvements: Enhanced data augmentation, class-weighted loss, Mixup, more unfrozen layers.
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