Instructions to use google/siglip-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-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/siglip-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 AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-224") - Notebooks
- Google Colab
- Kaggle
Easy evaluation of the zero-shot capabilities of siglip
#3
by fhvilshoj - opened
Hi!
Thanks for your big efforts in training this model. It's really helping push forward the field of AI as shown by your number of downloads!
I wanted to show how we've evaluated the model against a bunch of others on a handful of medical datasets. It's super easy to add other datasets from HF as well.
We did it with this repo: https://github.com/encord-team/text-to-image-eval
Would be curious to hear how this stacks up against your findings!
