KentaItakura/Image-classification-using-oversampling-imagedatastore
This example shows how to classify images for imbalanced training dataset using oversampling
This project helps anyone training image classification models when they have an imbalanced dataset, meaning some categories have many more images than others. It takes your existing image dataset, which can be unevenly distributed, and balances it by intelligently replicating images in under-represented categories. The output is a trained deep learning model that performs better at classifying images across all categories, even those that were initially scarce.
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Use this if you are building an image classification system and your training data has significantly more examples for some classes than others, which can lead to poor performance on the minority classes.
Not ideal if your image datasets are already well-balanced across all classes or if you prefer a down-sampling approach to handle imbalanced data.
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MATLAB
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MIT
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Jul 28, 2021
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