Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/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("nateraw/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nateraw/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/nateraw/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nateraw/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nateraw/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 59f68a04c72aee5d26421be2f4f0fc5256362fe87c3b89735d9d153d497a6a54
- Size of remote file:
- 343 MB
- SHA256:
- fc443b145fcc3a09cf07eb28b94e9a989ec3f0e8f7255e1fa08c0953ed4bae91
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