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 /Mar27_16-25-26_dee86bb862cd /events.out.tfevents.1711556773.dee86bb862cd.484.0
- Xet hash:
- 968d358d66412a5f0584b73216327c6908f288a6b3c82823161de419b91eef38
- Size of remote file:
- 23.3 kB
- SHA256:
- 2af69da1693ed6f5152c5b601e358461f7ae7d6c3298bd091d9dd2ecfa530741
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