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
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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
| 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 | |