# 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

## 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