Instructions to use t7hom/vit-beans-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use t7hom/vit-beans-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="t7hom/vit-beans-classifier") 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("t7hom/vit-beans-classifier") model = AutoModelForImageClassification.from_pretrained("t7hom/vit-beans-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from t7hom/vit-beans-classifier: direct link, hf CLI and curl.
- Browser
- Download file 452 Bytes
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https://huggingface.co/t7hom/vit-beans-classifier/resolve/main/README.md
- Command line
-
hf download hf://t7hom/vit-beans-classifier/README.md
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curl -L -o README.md https://huggingface.co/t7hom/vit-beans-classifier/resolve/main/README.md
452 Bytes
metadata
tags:
- vision
- image-classification
- vit
- beans
datasets:
- beans
metrics:
- accuracy
- f1
pipeline_tag: image-classification
library_name: transformers
Vision Transformer for Bean Leaf Disease Classification
- Authors: s_102413_109626
- Backbone:
WinKawaks/vit-tiny-patch16-224 - Dataset:
beans - Classes:
angular_leaf_spot,bean_rust,healthy
Test Metrics
- Test Accuracy: 0.9609
- Test F1 Macro: 0.9615