/ Machine Learning

Class-Balanced Loss Based on Effective Number of Samples

Class-Balanced Loss Based on Effective Number of Samples

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:

samples

Thus, the loss function is defined as:

loss

Visualisation for effective number of samples

image

GitHub