基于小数据集的改进LeNet图像分类模型研究

Research on improved LeNet image classification model based on small datasets

将传统卷积神经网络应用于小数据集上,LeNet模型准确率低并且收敛速度慢,VggNet等模型存在过拟合问题.针对小数据集提出一种改进LeNet模型,该模型在LeNet基础上使用ReLU函数替换sigmoid来提高收敛速度,加入1*1卷积增加模型深度并利用其改变维度的特点来提高识别准确率,通过分解卷积和提出改进Dropout方法减少过拟合.结果表明:改进LeNet模型分类自制小龙虾数据集,比LeNet收敛速度快6000步并且准确率提高约15%,比VggNet和ResNet过拟合程度明显减少;将改进LeNet模型推广应用于开源数据集MNIST和Fashion-MNIST上,改进模型也有良好的表现.

The results of applying traditional convolutional neural networks to small datasets showed that LeNet model had the problems of low accuracy and slow convergence,other models like VggNet appeared over-fitting matters.An improved LeNet model for small datasets is proposed,which was mainly changed as follows.Firstly,sigmoid is replaced with ReLU to improve the convergence speed.Secondly,1*1 convolution is added to increase the depth of the model and improve recognition accuracy.Finally,an improved dropout method is proposed to reduce over-fitting.In the crayfish project,the proposed model converged 6000 steps faster and had a 15%higher accuracy than LeNet,while the degree of over-fitting was significantly reduced compared to VggNet and ResNet.The proposed LeNet model was also applied to the open source datasets MNIST and Fashion-MNIST,and the results show good performance.