Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600 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-1600 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-1600") 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-1600") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ecf482543d7668ea0fed4465f93432685a95faaccf08427d23d6372010e174b
- Size of remote file:
- 690 MB
- SHA256:
- d47dd6db65c049d0b0b96fc5bd435a623640b8a08e08e06c530b8438b3ad3229
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