Instructions to use Luna-Skywalker/corn_dtect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastai
How to use Luna-Skywalker/corn_dtect with fastai:
from huggingface_hub import from_pretrained_fastai learn = from_pretrained_fastai("Luna-Skywalker/corn_dtect") - timm
How to use Luna-Skywalker/corn_dtect with timm:
import timm model = timm.create_model("hf-hub:Luna-Skywalker/corn_dtect", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from Luna-Skywalker/corn_dtect: direct link, hf CLI and curl.
- Browser
- Download file 693 Bytes
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https://huggingface.co/Luna-Skywalker/corn_dtect/resolve/main/README.md
- Command line
-
hf download hf://Luna-Skywalker/corn_dtect/README.md
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curl -L -o README.md https://huggingface.co/Luna-Skywalker/corn_dtect/resolve/main/README.md
693 Bytes
metadata
tags:
- fastai
- pytorch
- timm
license: mit
metrics:
- accuracy
library_name: fastai
pipeline_tag: image-classification
Potato disease detection
This model is trained for detecting if the corn plant is healthy or if it has diseases like Common Rust, Gray Leaf Spot or Northern Leaf Blight
Enjoyy
Model card
A ConvNeXT model used for image classfication.
Model Details
- Model Type: Image classification
- Papers:
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Dataset: New Bangladeshi Crop Disease: https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease
- Original: https://github.com/facebookresearch/ConvNeXt