Instructions to use sofiane-isi/vit-beans-sofiane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofiane-isi/vit-beans-sofiane with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sofiane-isi/vit-beans-sofiane") 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("sofiane-isi/vit-beans-sofiane") model = AutoModelForImageClassification.from_pretrained("sofiane-isi/vit-beans-sofiane", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from sofiane-isi/vit-beans-sofiane: direct link, hf CLI and curl.
- Browser
- Download file 709 Bytes
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https://huggingface.co/sofiane-isi/vit-beans-sofiane/resolve/main/README.md
- Command line
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hf download hf://sofiane-isi/vit-beans-sofiane/README.md
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curl -L -o README.md https://huggingface.co/sofiane-isi/vit-beans-sofiane/resolve/main/README.md
709 Bytes
metadata
library_name: transformers
pipeline_tag: image-classification
tags:
- image-classification
- vit
- beans
datasets:
- AI-Lab-Makerere/beans
ViT Beans Classifier
Vision Transformer fine-tuned for bean leaf disease classification.
Repository
sofiane-isi/vit-beans-sofiane
Dataset
AI-Lab-Makerere/beans
Classes:
- angular_leaf_spot
- bean_rust
- healthy
Evaluation
- Accuracy: 0.96875
- Evaluation loss: 0.12236211448907852
Base model
google/vit-base-patch16-224-in21k
MLOps workflow
Dataset → preprocessing → fine-tuning → evaluation → Hugging Face Hub → Model Card → Space.
Limitations
Educational model fine-tuned specifically on the Beans dataset.