Image Classification
Transformers
Safetensors
English
siglip
deepfake
detection
SigLIP2
art
Synthetic
Instructions to use prithivMLmods/open-deepfake-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/open-deepfake-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/open-deepfake-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/open-deepfake-detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/open-deepfake-detection") - Notebooks
- Google Colab
- Kaggle
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> [!warning]
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⚠️ Model Deprecated: model component is no longer recommended for use because it's outdated
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# **open-deepfake-detection**
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> open-deepfake-detection is a vision-language encoder model fine-tuned from `siglip2-base-patch16-512` for binary image classification. It is trained to detect whether an image is fake or real using the *OpenDeepfake-Preview* dataset. The model uses the `SiglipForImageClassification` architecture.
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