Instructions to use facebook/metaclip-b32-400m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/metaclip-b32-400m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="facebook/metaclip-b32-400m") 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("facebook/metaclip-b32-400m") model = AutoModelForZeroShotImageClassification.from_pretrained("facebook/metaclip-b32-400m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4462297e80e1ee984c741745b72a049cd9c39ec395115cc324e778273cf4fa4d
- Size of remote file:
- 605 MB
- SHA256:
- c87e9b594770b859030915161106bfd5a3ebf783a23d445996b4b6a90898d527
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