Instructions to use nielsr/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nielsr/vit-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("nielsr/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("nielsr/vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dcc0a09e971a08c65be1d7bf04e1291501e69fdfaec9bef460f6e7732054f15
- Size of remote file:
- 346 MB
- SHA256:
- ee53e3b222a27dae6e302209731a0d345e1fd1c8534990016cea17b1fb1f1c7f
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