cassava-classifier 0.0.4

Cassava leaf disease classification using Deep neural network in Pytorch

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).

Web app

Swagger documentation for API

Installation

pip install cassava-classifier

Inference example

from 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.

Development Setup

Pre-commit hooks

This project uses pre-commit to run linting, formatting, and type-checking automatically before each commit.

# Install pre-commit
uv tool install pre-commit

# Install the git hooks
pre-commit install

# (Optional) Run against all files manually
pre-commit run --all-files

The hooks include:

• ruff — linting with auto-fix + formatting (replaces black, isort, flake8)
• pre-commit-hooks — YAML/JSON validation, merge conflict detection, trailing whitespace
• mypy — static type checking

Other details

• Training data can be found on the Kaggle competition page

• Streamlit app code can be found here.

Github discussion forum

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.

1. Vladimir Iglovikov for his wonderful article "I trained a model. What is next?" — archived copy

2. Y. Nakama for the baseline code.