1.完整项目描述和程序获取
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2.部分仿真图预览
3.算法概述
ResNet系列网络，图像分类领域的知名算法，经久不衰，历久弥新，直到今天依旧具有广泛的研究意义和应用场景。被业界各种改进，经常用于图像识别任务。ResNet-18，数字代表的是网络的深度，也就是说ResNet18 网络就是18层的吗？实则不然，其实这里的18指定的是带有权重的 18层，包括卷积层和全连接层，不包括池化层和BN层。图像分类（Image Classification）是计算机视觉中的一个基础任务，将图像的语义将不同图像划分到不同类别。很多任务也可以转换为图像分类任务。比如人脸检测就是判断一个区域内是否有人脸，可以看作一个二分类的图像分类任务。
4.部分源码
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tempLayers = [
additionLayer(2,"Name","res5b")
reluLayer("Name","res5b_relu")
globalAveragePooling2dLayer("Name","pool5")
fullyConnectedLayer(10,"Name","fc10")
softmaxLayer("Name","prob")
classificationLayer("Name","ClassificationLayer_predictions")];
LG = addLayers(LG,tempLayers);
% clean up helper variable
clear tempLayers;
LG = connectLayers(LG,"pool1","res2a_branch2a");
LG = connectLayers(LG,"pool1","res2a/in2");
LG = connectLayers(LG,"bn2a_branch2b","res2a/in1");
LG = connectLayers(LG,"res2a_relu","res2b_branch2a");
LG = connectLayers(LG,"res2a_relu","res2b/in2");
LG = connectLayers(LG,"bn2b_branch2b","res2b/in1");
LG = connectLayers(LG,"res2b_relu","res3a_branch2a");
LG = connectLayers(LG,"res2b_relu","res3a_branch1");
LG = connectLayers(LG,"bn3a_branch1","res3a/in2");
LG = connectLayers(LG,"bn3a_branch2b","res3a/in1");
LG = connectLayers(LG,"res3a_relu","res3b_branch2a");
LG = connectLayers(LG,"res3a_relu","res3b/in2");
LG = connectLayers(LG,"bn3b_branch2b","res3b/in1");
LG = connectLayers(LG,"res3b_relu","res4a_branch2a");
LG = connectLayers(LG,"res3b_relu","res4a_branch1");
LG = connectLayers(LG,"bn4a_branch1","res4a/in2");
LG = connectLayers(LG,"bn4a_branch2b","res4a/in1");
LG = connectLayers(LG,"res4a_relu","res4b_branch2a");
LG = connectLayers(LG,"res4a_relu","res4b/in2");
LG = connectLayers(LG,"bn4b_branch2b","res4b/in1");
LG = connectLayers(LG,"res4b_relu","res5a_branch2a");
LG = connectLayers(LG,"res4b_relu","res5a_branch1");
LG = connectLayers(LG,"bn5a_branch1","res5a/in2");
LG = connectLayers(LG,"bn5a_branch2b","res5a/in1");
LG = connectLayers(LG,"res5a_relu","res5b_branch2a");
LG = connectLayers(LG,"res5a_relu","res5b/in2");
LG = connectLayers(LG,"bn5b_branch2b","res5b/in1");
net = trainNetwork(XTrain, YTrainCat, LG, options);
save Res18.mat net
A732
