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_15-55-15_dbda3bb1da36 /events.out.tfevents.1711554952.dbda3bb1da36.335.0
- Xet hash:
- e57c9158549ae1b046d1777e1a34db6db7f3a1814454f172912db4e4913ad501
- Size of remote file:
- 5.41 kB
- SHA256:
- 8f7234a64fa853603cc1c36f5a5d41886a4ff28c77139c1b3eb61ee3adb14680
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