richardaecn/class-balanced-loss
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019
When training image classification models, this project helps scientists and machine learning practitioners overcome the common problem of imbalanced datasets, where some categories have many more examples than others. It takes a dataset like CIFAR or iNaturalist and applies a special 'class-balanced' weighting to the training process. The outcome is a more accurate classification model, especially for those rare categories.
616 stars. No commits in the last 6 months.
Use this if your image classification model struggles to accurately identify objects in categories that have very few examples compared to others.
Not ideal if your dataset is already perfectly balanced across all object categories or if you are not working with image classification tasks.
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616
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Language
Python
License
MIT
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Last pushed
Aug 29, 2021
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