Deep Residual Learning for Image Classification using Cross Validation

Abstract

Convolutional Neural Networks (CNN) are very common now especially in the image classification tasks as CNN’s have better classification accuracy than other techniques available in image classification. Another type of CNN called as Residual Neural Networks (RESNET) are gaining popularity because of better accuracy than normal CNN because of residual block available in it. In the present article the RESNET architecture is used for image classification on CIFAR-10 dataset using cross-validation approach that reflects a consistently better accuracy on the above dataset.