Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST

Overview

Random Erasing Data Augmentation

===============================================================

Examples

black white random
i1 i2 i3
i4 i5 i6

This code has the source code for the paper "Random Erasing Data Augmentation".

If you find this code useful in your research, please consider citing:

@inproceedings{zhong2020random,
title={Random Erasing Data Augmentation},
author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020}
}

Other re-implementations

[Official Torchvision in Transform]

[Pytorch: Random Erasing for ImageNet]

[Python Augmentor]

[Person_reID CamStyle]

[Person_reID_baseline + Random Erasing + Re-ranking]

[Keras re-implementation]

Installation

Requirements for Pytorch (see Pytorch installation instructions)

Examples:

CIFAR10

ResNet-20 baseline on CIFAR10: python cifar.py --dataset cifar10 --arch resnet --depth 20

ResNet-20 + Random Erasing on CIFAR10: python cifar.py --dataset cifar10 --arch resnet --depth 20 --p 0.5

CIFAR100

ResNet-20 baseline on CIFAR100: python cifar.py --dataset cifar100 --arch resnet --depth 20

ResNet-20 + Random Erasing on CIFAR100: python cifar.py --dataset cifar100 --arch resnet --depth 20 --p 0.5

Fashion-MNIST

ResNet-20 baseline on Fashion-MNIST: python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20

ResNet-20 + Random Erasing on Fashion-MNIST: python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20 --p 0.5

Other architectures

For ResNet: --arch resnet --depth (20, 32, 44, 56, 110)

For WRN: --arch wrn --depth 28 --widen-factor 10

Our results

You can reproduce the results in our paper:

 CIFAR10 CIFAR10 CIFAR100 CIFAR100 Fashion-MNIST Fashion-MNIST
Models  Base. +RE Base. +RE Base. +RE
ResNet-20  7.21 6.73 30.84 29.97 4.39 4.02
ResNet-32  6.41 5.66 28.50 27.18 4.16 3.80
ResNet-44  5.53 5.13 25.27 24.29 4.41 4.01
ResNet-56  5.31 4.89 24.82 23.69 4.39 4.13
ResNet-110  5.10 4.61 23.73 22.10 4.40 4.01
WRN-28-10  3.80 3.08 18.49 17.73 4.01 3.65

NOTE THAT, if you use the latest released Fashion-MNIST, the performance of Baseline and RE will slightly lower than the results reported in our paper. Please refer to the issue.

If you have any questions about this code, please do not hesitate to contact us.

Zhun Zhong

Liang Zheng

Owner
Zhun Zhong
Zhun Zhong
Testability-Aware Low Power Controller Design with Evolutionary Learning, ITC2021

Testability-Aware Low Power Controller Design with Evolutionary Learning This repo contains the source code of Testability-Aware Low Power Controller

Lee Man 1 Dec 26, 2021
Collection of in-progress libraries for entity neural networks.

ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio

25 Dec 01, 2022
Attention mechanism with MNIST dataset

[TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention ma

YeongHyeon Park 12 Jun 10, 2022
A Python package to process & model ChEMBL data.

insilico: A Python package to process & model ChEMBL data. ChEMBL is a manually curated chemical database of bioactive molecules with drug-like proper

Steven Newton 0 Dec 09, 2021
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

Instead, two models for appearance modeling are included, together with the open-source BAGS model and the full set of code for inference. With this code, you can achieve around 79 Oct 08, 2022

PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

Keon Lee 63 Jan 02, 2023
MultiTaskLearning - Multi Task Learning for 3D segmentation

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

2 Sep 22, 2022
CvT-ASSD: Convolutional vision-Transformerbased Attentive Single Shot MultiBox Detector (ICTAI 2021 CCF-C 会议)The 33rd IEEE International Conference on Tools with Artificial Intelligence

CvT-ASSD including extra CvT, CvT-SSD, VGG-ASSD models original-code-website: https://github.com/albert-jin/CvT-SSD new-code-website: https://github.c

金伟强 -上海大学人工智能小渣渣~ 5 Mar 07, 2022
The Official Repository for "Generalized OOD Detection: A Survey"

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

Jingkang Yang 338 Jan 03, 2023
Authors implementation of LieTransformer: Equivariant Self-Attention for Lie Groups

LieTransformer This repository contains the implementation of the LieTransformer used for experiments in the paper LieTransformer: Equivariant self-at

35 Oct 18, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
style mixing for animation face

An implementation of StyleGAN on Animation dataset. Install git clone https://github.com/MorvanZhou/anime-StyleGAN cd anime-StyleGAN pip install -r re

Morvan 46 Nov 30, 2022
Event-forecasting - Event Forecasting Algorithms With Python

event-forecasting Event Forecasting Algorithms Theory Correlating events in comp

Intellia ICT 4 Feb 15, 2022
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Do you want a RL agent nicely moving on Atari? Rainbow is all you need! This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains bo

Jinwoo Park (Curt) 1.4k Dec 29, 2022
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification Created by Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Ch

Yongming Rao 414 Jan 01, 2023
Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique

AOS: Airborne Optical Sectioning Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique that employs manned or unmanned airc

JKU Linz, Institute of Computer Graphics 39 Dec 09, 2022
a morph transfer UGATIT for image translation.

Morph-UGATIT a morph transfer UGATIT for image translation. Introduction 中文技术文档 This is Pytorch implementation of UGATIT, paper "U-GAT-IT: Unsupervise

55 Nov 14, 2022
Semi-supervised Implicit Scene Completion from Sparse LiDAR

Semi-supervised Implicit Scene Completion from Sparse LiDAR Paper Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZH

114 Nov 30, 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Click on the image to

4.2k Jan 01, 2023