semvec/pypi-packages huggingface dataset:
cassava-classifier 0.0.3
Cassava leaf disease classification using Deep neural network in Pytorch
License: MIT
Keywords: image classification,leaf disease classifier,pytorch

# Cassava leaf disease classification
[![PyPI version shields.io](https://img.shields.io/badge/pypi-0.0.2-blue)](https://pypi.org/project/cassava-classifier/) [![Downloads](https://pepy.tech/badge/cassava-classifier)](https://pepy.tech/project/cassava-classifier)

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)   `.


<img src="static/cassava.gif" alt="inference" style="width:80%;" />

- Training data can be found on the [Kaggle competition page](https://www.kaggle.com/c/cassava-leaf-disease-classification)

- Streamlit app code can be found [here](https://github.com/p-s-vishnu/cassava_app).

[Github discussion forum](https://github.com/p-s-vishnu/cassava-leaf-disease-classification/discussins)



## Installation

`pip install cassava-classifier`



## Inference example

```python
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.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gPLY6nqF6P4WdvIRIAH_aYQn-iWkzvqs?usp=sharing) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/vpkprasanna/cassava-inference-from-pypi)



## 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.

1. Teranus
2. Nakama


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