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

GitHub Repository

Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

Created by Olga Sutyrina, Sarah Elemili, Abduragim Shtanchaev and Artur Bille Individual Tree Crown classification on WorldView-2 Images using Autoenc

2 Dec 08, 2022

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

297 Dec 27, 2022

Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

3 Feb 08, 2022

A machine learning malware analysis framework for Android apps.

🕵️ A machine learning malware analysis framework for Android apps. ☢️ DroidDetective is a Python tool for analysing Android applications (APKs) for p

77 Dec 27, 2022

Official implementation of the ICLR 2021 paper

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

272 Dec 28, 2022

Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Variance-Aware-MT-Test-Sets Variance-Aware Machine Translation Test Sets License See LICENSE. We follow the data licensing plan as the same as the WMT

5 Dec 21, 2021

An Open Source Machine Learning Framework for Everyone

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

170.1k Jan 04, 2023

A simple configurable bot for sending arXiv article alert by mail

arXiv-newsletter A simple configurable bot for sending arXiv article alert by mail. Prerequisites PyYAML=5.3.1 arxiv=1.4.0 Configuration All config

21 Nov 09, 2022

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementati

304 Jan 03, 2023

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open

19.4k Jan 04, 2023

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

DANN A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation Prerequisites Linux or OSX NVIDIA GPU + CUDA (may CuDNN) and corre

8 Apr 16, 2022

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

Related tags

Overview

Class-balanced-loss-pytorch

Dependencies

Review article of the paper

How it works

References

Owner

Vandit Jain

Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

Data-depth-inference - Data depth inference with python

A machine learning malware analysis framework for Android apps.

Official implementation of the ICLR 2021 paper

Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

An Open Source Machine Learning Framework for Everyone

A simple configurable bot for sending arXiv article alert by mail

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

Multi agent DDPG algorithm written in Python + Pytorch

DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

The authors' official PyTorch SigWGAN implementation

Python script to download the celebA-HQ dataset from google drive

The UI as a mobile display for OP25

SNE-RoadSeg in PyTorch, ECCV 2020

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset