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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from francis-ogbuagu/maize_vit_model: direct link, hf CLI and curl.
- Browser
- Download file 569 Bytes
-
https://huggingface.co/francis-ogbuagu/maize_vit_model/resolve/main/README.md
- Command line
-
hf download hf://francis-ogbuagu/maize_vit_model/README.md
-
curl -L -o README.md https://huggingface.co/francis-ogbuagu/maize_vit_model/resolve/main/README.md
569 Bytes
metadata
license: apache-2.0
language:
- en
metrics:
- accuracy
- precision
- f1
- recall
base_model:
- google/vit-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Agriculture
- Maize
- Maize Disease
- Maize Disease Detection
- Classification
model-index:
- name: maize_vit_model
results:
- task:
type: image-classification
dataset:
type: image
name: corn-or-maize-leaf-disease-dataset
metrics:
- name: accuracy
type: accuracy
value: 97.13