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")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
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README.md
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- beans
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metrics:
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- accuracy
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model-index:
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- name: vit-base-beans
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results:
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- beans
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metrics:
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- accuracy
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widget:
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- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg
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example_title: Healthy
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- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg
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example_title: Angular Leaf Spot
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- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg
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example_title: Bean Rust
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model-index:
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- name: vit-base-beans
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results:
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