Instructions to use google/siglip2-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-base-patch16-224") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/siglip2-base-patch16-224", dtype="auto") - Notebooks
- Google Colab
- Kaggle
question about 'model_type' in config.json
#5
by XA-hyy - opened
why param "model_type" in config.json is siglip,siglip_text_model and siglip_vision_model, not siglip2, siglip2_text_model and siglip2_vision_model?I noticed existed model class 'Siglip2Model',but still use 'SiglipModel'
Hello @qubvel-hf ,Could you provide me a text script to reproduce the flickr30k indicator in the paper(T2I Recall@1 is 85%)? My test indicator T2I Recall@1 is only 77%(siglip2-large-patch16-384),thanks!
Hi, I still got a question here. I found it is not compatible with siglip v1. patch embedding uses conv2d in v1 but uses dense in v2.