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agasta
/
virtus

Image Classification
Transformers
Safetensors
PyTorch
vit
computer-vision
deepfake-detection
binary-classification
Model card Files Files and versions
xet
Community

Instructions to use agasta/virtus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use agasta/virtus with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="agasta/virtus")
    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("agasta/virtus")
    model = AutoModelForImageClassification.from_pretrained("agasta/virtus")
  • Notebooks
  • Google Colab
  • Kaggle
virtus
343 MB
Ctrl+K
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  • 1 contributor
History: 5 commits
agasta's picture
agasta
Update README.md
acd0690 verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    4.98 kB
    Update README.md about 1 year ago
  • config.json
    716 Bytes
    Upload ViTForImageClassification about 1 year ago
  • model.safetensors
    343 MB
    xet
    Upload ViTForImageClassification about 1 year ago
  • preprocessor_config.json
    353 Bytes
    Upload feature extractor about 1 year ago