The idea of this project is to build an image classifier to find out healthy and diseased cassava leaves.
There are 4 different classes of leaf diseases namely - Cassava Bacterial Blight (CBB),Cassava Brown Streak Disease (CBSD),Cassava Green Mottle (CGM) and Cassava Mosaic Disease (CMD)
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pip install cassava-classifier
import PIL import Image
from cassava.pretrained import get_model
image = Image.open("<insert your image path here>")
# Use cassava.list_models() to list of available trained models
model = get_model(name:str)
model.predict_as_json(image: np.array)
>> {"class_name":str, "confidence": np.float}
Try out the inference code either on google colab or kaggle.
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Training data can be found on the Kaggle competition page
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Streamlit app code can be found here.
[Medium link]
We would like to thank Kaggle community as a whole for providing an avenue to learn and discuss latest data science/machine learning advancements but a hat tip to whose code was used / who inspired us.
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Vladimir Iglovikov for his wonderful article "I trained a model. What is next?"
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Y. Nakama for the baseline code.