Instructions to use yaxue/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yaxue/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yaxue/vit-base-beans") 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("yaxue/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("yaxue/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yaxue/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/yaxue/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yaxue/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yaxue/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- ed10bdcc8ebe89e3cee249ccd7f7de0c2a3f19a11885d57b33566cd3362c4e5f
- Size of remote file:
- 343 MB
- SHA256:
- 25afabc54810c83bbf9f0ff6ddc099068c8fb5d97f2c94b1479c715c2c44ea94
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