Instructions to use Wvolf/ViT_Deepfake_Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wvolf/ViT_Deepfake_Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection") 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("Wvolf/ViT_Deepfake_Detection") model = AutoModelForImageClassification.from_pretrained("Wvolf/ViT_Deepfake_Detection") - Inference
- Notebooks
- Google Colab
- Kaggle
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- recall
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library_name: transformers
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pipeline_tag: image-classification
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- recall
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library_name: transformers
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pipeline_tag: image-classification
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---
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<p>This model was trained by Rudolf Enyimba in partial fulfillment of the requirements
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of Solent University for the degree of MSc Artificial Intelligence and Data Science</p>
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<p>This model was trained to detect deepfake images.</p>
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<p>The model achieved an accuracy of <strong>98.67%</strong> on the test set.</p>
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<p>Upload a face image or pick from the samples below to test model accuracy</p>
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