Image Classification
Transformers
Safetensors
timm
vit
detection
deepfake
forensics
deepfake_detection
community
opensight
Instructions to use buildborderless/CommunityForensics-DeepfakeDet-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="buildborderless/CommunityForensics-DeepfakeDet-ViT") 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("buildborderless/CommunityForensics-DeepfakeDet-ViT") model = AutoModelForImageClassification.from_pretrained("buildborderless/CommunityForensics-DeepfakeDet-ViT") - timm
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with timm:
import timm model = timm.create_model("hf_hub:buildborderless/CommunityForensics-DeepfakeDet-ViT", pretrained=True) - Inference
- Notebooks
- Google Colab
- Kaggle
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base_model:
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- timm/vit_small_patch16_384.augreg_in21k_ft_in1k
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library_name: timm
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# Trained on 2.7M samples across 4,803 generators (see Training Data)
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base_model:
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- timm/vit_small_patch16_384.augreg_in21k_ft_in1k
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library_name: timm
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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example_title: Tiger
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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example_title: Teapot
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# Trained on 2.7M samples across 4,803 generators (see Training Data)
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