Cassava leaf disease classification
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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Training data can be found on the Kaggle competition page
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Streamlit app code can be found here.
Installation
pip install cassava-classifier
Inference example
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 on either google colab or kaggle.
Training pipeline
1.Model Architecture - Efficeientnet-B4 , Noisy Weights
2.Image Size - 512
3.Optimizer - Adam
4.Scheduler - GradualWarumUpScheduler
5.Loss - Focal Cosine Loss
6.Augmentations - Hard Augmentations
7.Epochs - 10
8.Early Stopping - No
9.Mixed Precision - Yes
Blog
[Medium link]
Acknowledgements
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.
- Teranus
- Nakama