Instructions to use darkenergy/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darkenergy/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="darkenergy/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("darkenergy/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("darkenergy/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from darkenergy/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/darkenergy/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://darkenergy/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/darkenergy/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 21f9e59c0c4bd178cd5859a2eef73047f3abcf20ec23c7fe0c1f7ceeda3a2183
- Size of remote file:
- 343 MB
- SHA256:
- a252cee07e1a600991a40e4a2e0ef4860fc3f56688b1f5ad8d2207f01dbd83e0
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