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Cassava leaf disease classification using Deep neural network in Pytorch

Project description

Cassava leaf disease classification

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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) .


Github discussion forum


pip install cassava-classifier

Inference example

import PIL import Image
from cassava.pretrained import get_model

image ="<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.

Open In Colab 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


[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.

  1. Teranus
  2. Nakama

Project details

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cassava_classifier-0.0.3.tar.gz (12.3 kB view hashes)

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