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.1711557673.dee86bb862cd.484.1
- Xet hash:
- 194d4faf4754ed4c34fcd4eb4f7968d1b2ec3831155ec7b441ce6cc3f62a5e71
- Size of remote file:
- 557 Bytes
- SHA256:
- b72496a738334d8ba2b4a7f5a4bddb548aba6cafd748a18ed4afba24f465eb9f
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