《Tab.2 Test set accuracy rate for CIFAR-10of various methods》

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《Convolutional Neural Network Based on Spatial Pyramid for Image Classification》

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The CIFAR-10 database is composed of 10classes of natural images split into 50 000 train images and 10 000 test images.Each image is a RGB image of 32×32 pixel.For the database，we make them in the range[0 1]and then make it gray.In Tab.2，CNN has the result of 52.06%when the learning rate is set 0.1.And LeNet-5 gets the result of 10%and can't recognize the database.Compared to CNN，LeNet-5 has one more pooling layer，and the last average pooling layer maybe miss some features.Our method achieves the result of64.26%when the learning rate is set 0.5.It is the best one among all methods.Although the three methods results are not well，the accuracy of our method still exceed the other two a lot.