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_15-56-28_dbda3bb1da36 /events.out.tfevents.1711554998.dbda3bb1da36.335.1
- Xet hash:
- 04b820b17d06ece9649d9e9fd756d05d6ae096f780abe39cacc4a3c3a7287bcf
- Size of remote file:
- 28.6 kB
- SHA256:
- 5b8fb8f6c993e4c720ff6706efcf49f9aaecfb51a965edaf1cda6f77fbf549c5
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