Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-400 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-400 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gowitheflowlab/clip-base-patch16-supervised-mulitilingual-400") 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("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-400") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-400", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91b73304d973969902794af511ea1333c12f3e25faecb5b5ee6da60acad89a85
- Size of remote file:
- 599 MB
- SHA256:
- 1a6ce8812a58ca243e2c280ffffd2d37008751832a9273bf0026c2fa7cd24271
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