Zero-Shot Image Classification
Transformers
Safetensors
siglip2_vision_model
feature-extraction
vision
Instructions to use ostris/siglip2-base-patch16-style-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ostris/siglip2-base-patch16-style-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ostris/siglip2-base-patch16-style-naflex") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ostris/siglip2-base-patch16-style-naflex") model = AutoModel.from_pretrained("ostris/siglip2-base-patch16-style-naflex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da7f18f45352ea8233ae35fc4d74523ea18e65d5dd69585739e99f76daa2090b
- Size of remote file:
- 372 MB
- SHA256:
- d786f94dc0503e4794e00a3b8f58ab79c9aac6b158bb5a51234f2b3a92545098
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