Instructions to use vicgalle/clip-vit-base-patch16-photo-critique with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vicgalle/clip-vit-base-patch16-photo-critique with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="vicgalle/clip-vit-base-patch16-photo-critique") 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("vicgalle/clip-vit-base-patch16-photo-critique") model = AutoModelForZeroShotImageClassification.from_pretrained("vicgalle/clip-vit-base-patch16-photo-critique") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
CLIP model retrained over some subset of the DPC dataset
Usage instructions
from transformers import AutoTokenizer, AutoModel, CLIPProcessor
tokenizer = AutoTokenizer.from_pretrained("vicgalle/clip-vit-base-patch16-photo-critique")
model = AutoModel.from_pretrained("vicgalle/clip-vit-base-patch16-photo-critique", from_flax=True)
processor = CLIPProcessor.from_pretrained("vicgalle/clip-vit-base-patch16-photo-critique")
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