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bsvaz
/
landmark-classification-vit

Image Classification
Transformers
Safetensors
vit
Model card Files Files and versions
xet
Community

Instructions to use bsvaz/landmark-classification-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bsvaz/landmark-classification-vit with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="bsvaz/landmark-classification-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("bsvaz/landmark-classification-vit")
    model = AutoModelForImageClassification.from_pretrained("bsvaz/landmark-classification-vit")
  • Notebooks
  • Google Colab
  • Kaggle
landmark-classification-vit
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  • 1 contributor
History: 3 commits
bsvaz's picture
bsvaz
Add image processor config
75d48d6 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    5.17 kB
    Add Vision Transformer for landmark classification over 1 year ago
  • config.json
    3.58 kB
    Add Vision Transformer for landmark classification over 1 year ago
  • model.safetensors
    343 MB
    xet
    Add Vision Transformer for landmark classification over 1 year ago
  • preprocessor_config.json
    351 Bytes
    Add image processor config over 1 year ago