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
Update README.md
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by dewsy - opened
README.md
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from transformers import CLIPProcessor, CLIPModel
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model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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from transformers import CLIPProcessor, CLIPModel
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model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32", from_tf=True)
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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