Instructions to use CollectionStudio/siglip-so400m-patch14-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip-so400m-patch14-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip-so400m-patch14-224") 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 AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("CollectionStudio/siglip-so400m-patch14-224") model = AutoModelForZeroShotImageClassification.from_pretrained("CollectionStudio/siglip-so400m-patch14-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from CollectionStudio/siglip-so400m-patch14-224: direct link, hf CLI and curl.
- Browser
- Download file 798 kB
-
https://huggingface.co/CollectionStudio/siglip-so400m-patch14-224/resolve/main/spiece.model
- Command line
-
hf download hf://CollectionStudio/siglip-so400m-patch14-224/spiece.model
-
curl -L -o spiece.model https://huggingface.co/CollectionStudio/siglip-so400m-patch14-224/resolve/main/spiece.model
798 kB
- Xet hash:
- 80004a3b080eadaf5b5506765c7446744ce341114fc14fa4f833585f39624696
- Size of remote file:
- 798 kB
- SHA256:
- 1e5036bed065526c3c212dfbe288752391797c4bb1a284aa18c9a0b23fcaf8ec
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