Instructions to use Hemg/Deepfake-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Deepfake-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/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("Hemg/Deepfake-Detection") model = AutoModelForImageClassification.from_pretrained("Hemg/Deepfake-Detection") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -2
config.json
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Fake": "0",
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"Real": "1"
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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