《Tab.3 Test set accuracy rate for the vehicle of various methods》

《Tab.3 Test set accuracy rate for the vehicle of various methods》   提示：宽带有限、当前游客访问压缩模式
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《Convolutional Neural Network Based on Spatial Pyramid for Image Classification》


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The vehicle database is composed of 64×64pixel RGB images which are split into 13 491train images and 1 349test image.And each contains truck，car，bus and van（4classes）.For the database，we make them in the range[0 1]，and then make it gray.In Tab.3，CNN gets the result of52.34%when the learning rate is 0.1.And LeNet-5gets the result of 25%.LeNet-5can't recognize the database.The results wouldn't change when the learning rate is changed.It shows that LeNet-5's applicability is narrow.Our method achieves the result of 79.26%when learning rate is 1.It is the best accuracy among the three methods.

图表编号XD003876500    严禁用于非法目的
绘制时间2018.12.01
作者Gaihua Wang、Meng Lü、Tao Li、Guoliang Yuan、Wenzhou Liu
绘制单位Hubei Collaborative Innovation Centre for High-Efficiency Utilization of Solar Energy,Hubei University of Technology、School of Electrical and Electronic Engineering,Hubei University of Technology、School of Electrical and Electronic Engineering,Hubei Unive
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