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pytorch-ricap's Introduction

A PyTorch implementation of RICAP

This repository contains code for a data augmentation method RICAP (Random Image Cropping And Patching) based on Data Augmentation using Random Image Cropping and Patching for Deep CNNs implemented in PyTorch.

example

Requirements

  • Python 3.6
  • PyTorch 0.4 or 1.0

Training

CIFAR-10

WideResNet28-10 baseline on CIFAR-10:

python train.py --dataset cifar10

WideResNet28-10 +RICAP on CIFAR-10:

python train.py --dataset cifar10 --ricap True

WideResNet28-10 +Random Erasing on CIFAR-10:

python train.py --dataset cifar10 --random-erase True

WideResNet28-10 +Mixup on CIFAR-10:

python train.py --dataset cifar10 --mixup True

Results

Model Error rate Loss Error rate (paper)
WideResNet28-10 baseline 3.82 0.158 3.89
WideResNet28-10 +RICAP 2.82 0.141 2.85
WideResNet28-10 +Random Erasing 3.18 0.114 4.65
WideResNet28-10 +Mixup 3.02 0.158 3.02

Learning curves of loss and accuracy.

loss

acc

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