Instructions to use openai/clip-vit-large-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/clip-vit-large-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-large-patch14-336") 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-large-patch14-336") model = AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-large-patch14-336") - Notebooks
- Google Colab
- Kaggle
Add widget example input (#3)
Browse files- Add widget example input (2d735168d1fe2c7511c0d02f80c4cc08693e7fd5)
README.md
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---
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tags:
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- generated_from_keras_callback
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model-index:
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- name: clip-vit-large-patch14-336
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results: []
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---
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tags:
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- generated_from_keras_callback
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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candidate_labels: playing music, playing sports
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example_title: Cat & Dog
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model-index:
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- name: clip-vit-large-patch14-336
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results: []
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