An improved residual network model for image recognition using a combination of snapshot ensembles and the cutout technique

NUF-Net (Naresuan University and Fiber One Public Company Limited Network) is a new and improved Convolutional Neural Network (CNN) model based on the previously developed NU-LiteNet model. Improvements in accuracy were achieved by adding the identity mapping technique of the ResNet model and incorporating Snapshot Ensembles and the Cutout technique into the NU-LiteNet model. We modified the structure of the convolution layers by changing any filters of a size larger than 3 x3, into a 3 x3 filter, thereby significantly reducing processing time and reducing the error rate. To test the effectiveness of our modifications, we developed 10 variations of the NUF-Net-Residual model, one of which, termed NUF-Net-Residual-102, achieved significantly lower error rates than both ResNet and Wide-ResNet when using CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets. The relative error rates were 2.94% for CIFAR-10, 17.57% for CIFAR-100 and 29.57% for Tiny-ImageNet. As well, NUF-Net-Residual-102 achieved a model parameter size of 31.65 million which is a lower value than for Wide-ResNet-32 (46.16 million), although higher than ResNet-1202 (19.42 million).