Instructions to use facebook/levit-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/levit-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/levit-128") 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-128") model = AutoModelForImageClassification.from_pretrained("facebook/levit-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 848a00b5565f06625ff69fc5cc559218491157dc893260067768b986e6fd92e5
- Size of remote file:
- 38.6 MB
- SHA256:
- dff350c0b19ec202c30ffc79c9ec32c1311fe581d3d3a1670b3c52503196f868
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