Instructions to use facebook/levit-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/levit-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/levit-384") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/levit-384") model = AutoModelForImageClassification.from_pretrained("facebook/levit-384") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3c24a1f5ce8389705dd136fcd0bd76589a5f5a34481ba870f7b812302705f6c
- Size of remote file:
- 158 MB
- SHA256:
- 97f30fb523f5a099142105dc2dd6995d7c3d048cb15d62abbc315b00b7a42276
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