Image Classification
Transformers
ONNX
Safetensors
English
vit
Agriculture
Maize
Maize Disease
Maize Disease Detection
Classification
Eval Results (legacy)
Instructions to use francis-ogbuagu/maize_vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use francis-ogbuagu/maize_vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="francis-ogbuagu/maize_vit_model") 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("francis-ogbuagu/maize_vit_model") model = AutoModelForImageClassification.from_pretrained("francis-ogbuagu/maize_vit_model") - Notebooks
- Google Colab
- Kaggle
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Model tree for francis-ogbuagu/maize_vit_model
Base model
google/vit-base-patch16-224Evaluation results
- accuracy on corn-or-maize-leaf-disease-datasetself-reported97.130