Instructions to use openai/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32") 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("openai/clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-base-patch32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e568e89c369583c1e8b39e08305a07e71f844d1cc99ee57ffc33ec11275a1eb2
- Size of remote file:
- 606 MB
- SHA256:
- 0d7e64ea2c496306a4bc0a6a7ab21e4755fd8c7beb01e6d9f5dc8d6662f1bfd2
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