Instructions to use Veritone/siglip2-so400m-patch16-256-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-so400m-patch16-256-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-so400m-patch16-256-text") 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("Veritone/siglip2-so400m-patch16-256-text") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-so400m-patch16-256-text") - Notebooks
- Google Colab
- Kaggle
File size: 132 Bytes
968105b | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
size 4241003
|