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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea7830c76701e44a361aba7a6903e01438a5a0f00c51750c883e7b049ffa85c9
- Size of remote file:
- 690 MB
- SHA256:
- 440c6f2be902d31fa23bcf4b687d9f8f7ef53fa9b344c3d5a10a4849fee1004b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.