Instructions to use davanstrien/vit-base-patch32-224-in21-leicester_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davanstrien/vit-base-patch32-224-in21-leicester_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="davanstrien/vit-base-patch32-224-in21-leicester_binary") 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("davanstrien/vit-base-patch32-224-in21-leicester_binary") model = AutoModelForImageClassification.from_pretrained("davanstrien/vit-base-patch32-224-in21-leicester_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model (#1)
- Librarian Bot: Add base_model information to model (e4bb8e284c6e3bd6d6f9a34c78814aa16fb6751d) Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>4d8b7c6
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