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

La source de mon module 'pyfade' disponible sur Pypi.

Version: 1.2 Introduction Pyfade est un module permettant de créer des dégradés colorés. Il vous permettra de changer chaque ligne de votre texte par

20 Sep 12, 2021

Distance Encoding for GNN Design

Distance-encoding for GNN design This repository is the official PyTorch implementation of the DEGNN and DEAGNN framework reported in the paper: Dista

172 Nov 08, 2022

Highway networks implemented in PyTorch.

PyTorch Highway Networks Highway networks implemented in PyTorch. Just the MNIST example from PyTorch hacked to work with Highway layers. Todo Make th

56 Dec 14, 2022

Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

ASFormer: Transformer for Action Segmentation This repo provides training & inference code for BMVC 2021 paper: ASFormer: Transformer for Action Segme

42 Dec 23, 2022

Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

27 Oct 15, 2022

Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

CMKN Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021. Testing Yo

1 Nov 17, 2021

Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"

Prompt-Tuning Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning" Currently, we support the following huggigface models: Bart

36 Dec 19, 2022

PyTorch Connectomics: segmentation toolbox for EM connectomics

Introduction The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individua

132 Dec 26, 2022

[NeurIPS2021] Code Release of K-Net: Towards Unified Image Segmentation

K-Net: Towards Unified Image Segmentation Introduction This is an official release of the paper K-Net:Towards Unified Image Segmentation. K-Net will a

423 Jan 02, 2023

Implementation of PersonaGPT Dialog Model

PersonaGPT An open-domain conversational agent with many personalities PersonaGPT is an open-domain conversational agent cpable of decoding personaliz

42 Jan 01, 2023

An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

194 Dec 15, 2022

Pytorch implementation of ICASSP 2022 paper Attention Probe: Vision Transformer Distillation in the Wild

Attention Probe: Vision Transformer Distillation in the Wild Jiahao Wang, Mingdeng Cao, Shuwei Shi, Baoyuan Wu, Yujiu Yang In ICASSP 2022 This code is

6 Sep 21, 2022

⚡ Fast • 🪶 Lightweight • 0️⃣ Dependency • 🔌 Pluggable • 😈 TLS interception • 🔒 DNS-over-HTTPS • 🔥 Poor Man's VPN • ⏪ Reverse & ⏩ Forward • 👮🏿 "Proxy Server" framework • 🌐 "Web Server" framework • ➵ ➶ ➷ ➠ "PubSub" framework • 👷 "Work" acceptor & executor framework

Table of Contents Features Install Using PIP Stable version Development version Using Docker Stable version Development version Using HomeBrew Stable

2.2k Jan 08, 2023

[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

47 Jan 09, 2023

An MQA (Studio, originalSampleRate) identifier for lossless flac files written in Python.

An MQA (Studio, originalSampleRate) identifier for "lossless" flac files written in Python.

10 Oct 03, 2022

Custom studies about block sparse attention.

Block Sparse Attention 研究总结本人近半年来对Block Sparse Attention（块稀疏注意力）的研究总结（持续更新中）。按时间顺序，主要分为如下三部分： PyTorch 自定义 CUDA 算子——以矩阵乘法为例基于 Triton 的 Block Sparse A

2 Jan 09, 2022

The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

134 Jan 01, 2023

Example how to deploy deep learning model with aiohttp.

aiohttp-demos Demos for aiohttp project. Contents Imagetagger Deep Learning Image Classifier URL shortener Toxic Comments Classifier Moderator Slack B

661 Jan 04, 2023

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Xcessiv Xcessiv is a tool to help you create the biggest, craziest, and most excessive stacked ensembles you can think of. Stacked ensembles are simpl

1.3k Nov 17, 2022

Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Sparse Steerable Convolution (SS-Conv) Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and

25 Dec 21, 2022