Instructions to use voloshka3/beans-deit-tiny-lab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voloshka3/beans-deit-tiny-lab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="voloshka3/beans-deit-tiny-lab") 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("voloshka3/beans-deit-tiny-lab") model = AutoModelForImageClassification.from_pretrained("voloshka3/beans-deit-tiny-lab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from voloshka3/beans-deit-tiny-lab: direct link, hf CLI and curl.
- Browser
- Download file 662 Bytes
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https://huggingface.co/voloshka3/beans-deit-tiny-lab/resolve/main/README.md
- Command line
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hf download hf://voloshka3/beans-deit-tiny-lab/README.md
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curl -L -o README.md https://huggingface.co/voloshka3/beans-deit-tiny-lab/resolve/main/README.md
662 Bytes
metadata
pipeline_tag: image-classification
datasets:
- AI-Lab-Makerere/beans
tags:
- image-classification
- pytorch
- transformers
Bean Leaf Disease Classifier
Fine-tuned from facebook/deit-tiny-patch16-224 using the Beans dataset.
Classes
angular_leaf_spot, bean_rust, healthy
Training
- 600 training images
- 2 epochs
- Two learning-rate experiments tracked with MLflow
- Best model selected using validation accuracy
Evaluation
- Validation accuracy: 0.9549
- Test accuracy: 0.9297
- Test macro F1: 0.9302
Limitations
This is an educational model evaluated on a small dataset. It is not validated for operational agricultural diagnosis.