Instructions to use glrh11/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glrh11/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glrh11/vit-base-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("glrh11/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("glrh11/vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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tags:
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- vision
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- image-classification
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datasets:
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# Vision Transformer (base-sized model)
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license: apache-2.0
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tags:
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- vision
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- image-classification
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datasets:
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- imagenet-1k
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- imagenet-21k
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widget:
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- src: >-
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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example_title: Tiger
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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example_title: Teapot
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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example_title: Palace
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# Vision Transformer (base-sized model)
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