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")# 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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f3aa08ef7e2b71eb3abc6b04634fb91b16c0d658118e45bd3a35b766fb7c7429
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size 349850288
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