Matlab yolov2 深度學習物體檢測 超級簡單代碼

在深度學習的物體檢測方面，相比其他平臺，MATLAB打包好了很多模塊方法和網絡，對於簡單的應用，已經足夠應付！大家跟着調用，稍微修改一下適應的參數就可以啦！！下面我手把手教學！！！

1.需要讀取提前製作的csv文件（裏面有training data的bounding box的座標),csv 代碼會在另外一篇文章介紹
然後對csv裏的數據進行讀取,並轉化數據
A = cell2mat© 將元胞數組轉換爲普通數組。元胞數組的元素必須全都包括相同的數據類型，並且生成的數組也是該數據類型。

close all;clear;clc
gpuDevice(1);
GDSDataset = readtable('./train_cell2_matlab_0525.csv','Delimiter',',');
%將表格裏的座標數據轉化爲double類型，原來的csv文件在
for i=1:length(GDSDataset{:,1})
    GDSDataset{i,2} = {str2double(reshape(strsplit(cell2mat(GDSDataset{i,2})),4,[])')};
end

2.這一步會抓取其中一張圖像文件根據csv的座標製作boundingbox並展示

%%%%%%展示其中一張圖片%%%%%%%
% Add the fullpath to the local vehicle data folder.
% % Read one of the images.
%讀取CSV文件裏的路徑列fn的第三張圖片
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.最重要的一步訓練步驟

% %%%%%%%%%%%%%Set Training and Validation Split%%%%%%%%%%%%%%%
% % Set random seed to ensure example training reproducibility.
% rng(0);
% % 隨機的將數據集分爲訓練集和驗證集
% shuffledIdx = randperm(height(windowDataset));
90%的測試集和10%的驗證集
idx = floor(0.9 * height(windowDataset));
trainingData = windowDataset(shuffledIdx(1:idx),:);
testData = windowDataset(shuffledIdx(idx+1:end),:);

epoch,batchsize,iteration大家很容易弄混淆，我來舉個例子
epoch是指遍歷一次所有樣本的行爲
batchsize是指針對一個小子集做一次梯度下降
比如總共1000個樣本，batchsize是50，則有20個iterations，20個iterations完成一個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

4.將生成的模型做一個快速測試

%%%%%%%%%%%%%%%%%%%%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)