Instructions to use Thouph/clip-vit-large-patch14-224-datacomp-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thouph/clip-vit-large-patch14-224-datacomp-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Thouph/clip-vit-large-patch14-224-datacomp-hf") 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("Thouph/clip-vit-large-patch14-224-datacomp-hf") model = AutoModelForZeroShotImageClassification.from_pretrained("Thouph/clip-vit-large-patch14-224-datacomp-hf") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55196eb2e4220da0070895077e9a9fa2471addfd6b27d18700e8b8a030198ca2
- Size of remote file:
- 1.71 GB
- SHA256:
- 6bc89bdf2c5d1d87480e96bcb9a6bb829704186e1fa6a286e63e826b7b0e2dad
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