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_13-42-18_16ca11c6137e /events.out.tfevents.1714831102.16ca11c6137e.960.1
- Xet hash:
- e363256226a5efbca9fd8668d69f0ac309e62a17657177beb0126c1b8617e929
- Size of remote file:
- 557 Bytes
- SHA256:
- 7b3e1eae5afbf79f9fa2204d436b477d28092060c28145da9d35243e86b61f7e
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