Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

Last update: Dec 29, 2022

Overview

Class-balanced-loss-pytorch

Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19.

Yin Cui, Menglin Jia, Tsung-Yi Lin(Google Brain), Yang Song(Google), Serge Belongie

Dependencies

Python (>=3.6)
Pytorch (>=1.2.0)

Review article of the paper

Medium Article

How it works

It works on the principle of calculating effective number of samples for all classes which is defined as:

Thus, the loss function is defined as:

Visualisation for effective number of samples

References

official tensorflow implementation

Owner

Vandit Jain

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