Instructions to use facebook/levit-192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/levit-192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/levit-192") 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-192") model = AutoModelForImageClassification.from_pretrained("facebook/levit-192", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 483545ff123fdda06c75e6615b8a676fc982cf790064e5ee9a7e77147b8e825e
- Size of remote file:
- 45.5 MB
- SHA256:
- fa02c2e845ec799e9b97e7bbc3d2bb72501dd1accac9fcae4918dfca2fe1fe12
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