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model_1.safetensors - ConvNeXt-Large (788 MB)model_2.safetensors - ViT-Large (1220 MB)model_3.safetensors - Swin-Large (783 MB)ensemble.pth - Ensemble wrapper weightsconfig.json - Configurationinference_example.py - Usage example1# See inference_example.py for complete code
2# Load all 3 models, run predictions, compute weighted average| Model | Backbone | F1 Score | Ensemble Weight |
|---|---|---|---|
| Model 13.1 | ConvNeXt-Large | 0.8971 | 0.25 |
| Model 13.2 | ViT-Large | 0.9382 | 0.35 |
| Model 13.3 | Swin-Large | 0.9586 | 0.40 |
torch>=2.0.0
timm>=0.9.0
torchvision>=0.15.0
safetensors
pillow1@model{v14-deepfake-detector,
2 title={DeepFake Detector V14},
3 author={Ash},
4 year={2024},
5 publisher={Hugging Face}
6}ash12321/deepfake-detector-v13