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_13-45-02_9f063c349a98 /events.out.tfevents.1714831605.9f063c349a98.602.1
- Xet hash:
- c8715b504539d04cd4cde77c4f22103cd91ca68e9cfc54c038b6a0670f25d05d
- Size of remote file:
- 566 Bytes
- SHA256:
- 499c3af45eb8da65143a0ec8b993064a71c95ff8373b00a9c357b1a44128eec5
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