Instructions to use TJKlein/CLIP-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TJKlein/CLIP-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TJKlein/CLIP-ViT") 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("TJKlein/CLIP-ViT") model = AutoModelForZeroShotImageClassification.from_pretrained("TJKlein/CLIP-ViT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c79273d42d2fca376f15256f2feb3077ee6be54aa85a89e679487c1d47d39e17
- Size of remote file:
- 1.71 GB
- SHA256:
- 14a6f32ef2d4c9266f782f5c9292715bed92842ef73b69d59a93bac4127eec19
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