Cifar10 is a widely used dataset for image classification , which contains 60k RGB images of size 32 × 32 categorized into 10 classes . The dataset is partitioned into a training set with 50k images and a test set with 10k images . Furthermore , data augmentation is applied to every training image , with padding 4 pixels to each side and randomly sampling a 32 × 32 crop . ResNet ( He et al . , 2015 ) , a well - known effective CNN model for image recognition , is adopted to perform classification on CIFAR-10 . Concretely speaking , we use ResNet32 and ResNet110 models , respectively containing 32 and 110 layers . The code is based on a public Lasagne implementation 3 . The mini - batch size is set as M = 128 and Momentum - SGD Sutskever et al . ( 2013 ) is used as the optimization algorithm . Following the learning rate scheduling strategy in the original paper ( He et al . , 2015 ) , we set the initial learning rate as 0.1 and multiply it by a factor of 0.1 after the 32k - th and 48k - th model update . Training in this way the test accuracy reaches about 92.4 % and 93.2 % , respectively for ResNet32 and ResNet110 .