Instructions to use CollectionStudio/siglip2-so400m-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip2-so400m-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip2-so400m-patch16-256") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CollectionStudio/siglip2-so400m-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
10 days ago
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Duplicate from google/siglip2-so400m-patch16-256
Co-authored-by: Pavel Iakubovskii <qubvel-hf@users.noreply.huggingface.co>7dcc6f7