Matlab|Matlab yolov2 深度学习物体检测超级简单代码 Matlabyolov2深度学习物体检测超

在深度学习的物体检测方面，相比其他平台，MATLAB打包好了很多模块方法和网络，对于简单的应用，已经足够应付！大家跟着调用，稍微修改一下适应的参数就可以啦！！下面我手把手教学！！！
1.需要读取提前制作的csv文件（里面有training data的bounding box的坐标),csv 代码会在另外一篇文章介绍
然后对csv里的数据进行读取,并转化数据
A = cell2mat? 将元胞数组转换为普通数组。元胞数组的元素必须全都包括相同的数据类型，并且生成的数组也是该数据类型。

close all; clear; clc gpuDevice(1); GDSDataset = readtable('./train_cell2_matlab_0525.csv','Delimiter',','); %将表格里的坐标数据转化为double类型，原来的csv文件在 for i=1:length(GDSDataset{:,1}) GDSDataset{i,2} = {str2double(reshape(strsplit(cell2mat(GDSDataset{i,2})),4,[])')}; end

2.这一步会抓取其中一张图像文件根据csv的坐标制作boundingbox并展示

%%%%%%展示其中一张图片%%%%%%% % Add the fullpath to the local vehicle data folder. % % Read one of the images. %读取CSV文件里的路径列fn的第三张图片 I = imread(GDSDataset.fn{3}); % Insert the ROI labels. I = insertShape(I, 'Rectangle', GDSDataset.cell2{3}); % Resize and display image. % I = imresize(I,2); figure imshow(I)

3.最重要的一步训练步骤

% %%%%%%%%%%%%%Set Training and Validation Split%%%%%%%%%%%%%%% % % Set random seed to ensure example training reproducibility. % % Set random seed to ensure example training reproducibility. rng(0); % % Randomly split data into a training and test set. % shuffledIdx = randperm(height(GDSDataset)); % idx = floor(0.9 * height(GDSDataset)); % trainingData = https://www.it610.com/article/GDSDataset(shuffledIdx(1:idx),:); % testData = GDSDataset(shuffledIdx(idx+1:end),:); trainingData = GDSDataset

epoch,batchsize,iteration大家很容易弄混淆，我来举个例子
epoch是指遍历一次所有样本的行为
batchsize是指针对一个小子集做一次梯度下降
比如总共1000个样本，batchsize是50，则有20个iterations，20个iterations完成一个epoch.

%%%%%%%%%%%%%Set Training Options%%%%%%%%%%%%%%%%%%%%%%% % Options for step 1. imageSize = [1536 2048 3]; numClasses =width(GDSDataset)-1; %需要生成boudingbox的anchorboxes anchorBoxes = [93,172; 100,180; 344,628]; baseNetwork = resnet50; % Specify the feature extraction layer. featureLayer = 'activation_40_relu'; % Create the YOLO v2 object detection network. lgraph = yolov2Layers(imageSize,numClasses,anchorBoxes,baseNetwork,featureLayer); %%%%%%%%%%%%%Train YOLO v2 Object Detector%%%%%%%%%% doTraining = true; model_name = 'standard_cell2_detector_yolov2_0611_epoch50_changeanchor_testr'; if doTraining% Configure the training options. %* Lower the learning rate to 1e-3 to stabilize training. %* Set CheckpointPath to save detector checkpoints to a temporary %location. If training is interrupted due to a system failure or %power outage, you can resume training from the saved checkpoint. options = trainingOptions('sgdm', ... 'MiniBatchSize', 2, .... 'InitialLearnRate',1e-5, ... 'MaxEpochs',10,... 'CheckpointPath', tempdir, ... 'Shuffle','every-epoch'); tic; % Train YOLO v2 detector. [detector,info] = trainYOLOv2ObjectDetector(trainingData,lgraph,options); trainingTime = toc; save (model_name, 'detector','-v7.3'); else % Load pretrained detector for the example. pretrained = load('standard_cell2_detector_yolov2_0610_epoch50_2.mat'); detector = pretrained.detector; end

【Matlab|Matlab yolov2 深度学习物体检测超级简单代码】4.将生成的模型做一个快速测试

%%%%%%%%%%%%%%%%%%%%As a quick test, run the detector on one test image. % Read a test image. I = imread('/home/testdata_crop_800_800/2.png'); % Run the detector. [bboxes,scores] = detect(detector,I)% Annotate detections in the image. I = insertObjectAnnotation(I,'rectangle',bboxes,scores); imshow(I)