Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

timm
/
vit_base_patch16_siglip_256.v2_webli

Image Feature Extraction
timm
PyTorch
Safetensors
Transformers
siglip
siglip2
Model card Files Files and versions
xet
Community
1

Instructions to use timm/vit_base_patch16_siglip_256.v2_webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • timm

    How to use timm/vit_base_patch16_siglip_256.v2_webli with timm:

    import timm
    
    model = timm.create_model("hf_hub:timm/vit_base_patch16_siglip_256.v2_webli", pretrained=True)
  • Transformers

    How to use timm/vit_base_patch16_siglip_256.v2_webli with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch16_siglip_256.v2_webli")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("timm/vit_base_patch16_siglip_256.v2_webli", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Difference in crop/resize mode between timm config and open_clip config

#1 opened 10 months ago by
dhoa
Company
TOS Privacy About Careers
Website
Models Datasets Spaces Pricing Docs