Metadata-Version: 2.1
Name: cassava-classifier
Version: 0.0.3
Summary: Cassava leaf disease classification using Deep neural network in Pytorch
Home-page: https://github.com/p-s-vishnu/cassava-leaf-disease-classification
Author: Prasanna Kumar, PS Vishnu
License: MIT
Description: # 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
        
        
        <a href="https://www.buymeacoffee.com/vpkprasanna" target="_blank"><img src="https://github.com/appcraftstudio/buymeacoffee/raw/master/Images/snapshot-bmc-button.png" width="300"></a>
        
Keywords: image classification,leaf disease classifier,pytorch
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
