Instructions to use molsen/beit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use molsen/beit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="molsen/beit") 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("molsen/beit") model = AutoModelForImageClassification.from_pretrained("molsen/beit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d257aafa322ee12fa363669150b1820859a7718e523bbd5642f1c38bdbb56b07
- Size of remote file:
- 347 MB
- SHA256:
- 6168324733477b542c3ce34a12f1fcf3679f97a63c5f8f13f4a70e5783f04101
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