Instructions to use HorcruxNo13/beit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/beit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/beit-base-patch16-224") 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("HorcruxNo13/beit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/beit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
beit-base-patch16-224 / runs /May04_13-42-18_16ca11c6137e /events.out.tfevents.1714830139.16ca11c6137e.960.0
- Xet hash:
- 3144e03677e3935a2a9f42ab1c6d2f6061a038f292f7a4cb35b5b895a5d217f1
- Size of remote file:
- 24 kB
- SHA256:
- 57580ffae8fd477338a41af3cac83e4e2ae8b85457b177e7ece3b2cd92c1a280
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