YOLO的Matlab代码

以下是YOLO的Matlab代码示例：

% Load the pretrained YOLOv2 network and its supporting files.
net = load('yolov2DAG.mat');
classNames = importdata('cocoClasses.txt');

% Read the input image.
I = imread('example.jpg');

% Resize the image to the input size of the YOLOv2 network.
inputSize = net.meta.inputSize;
I = imresize(I, inputSize(1:2));

% Run the YOLOv2 network on the input image.
tic
yPred = predict(net, I);
toc

% Identify the bounding boxes and class labels for the objects detected by YOLOv2.
anchors = net.meta.anchors;
numAnchors = size(anchors, 1);
numClasses = numel(classNames);
bboxParams = net.meta.bboxParams;
yPred = reshape(yPred, [numAnchors*(numClasses+5), inputSize(1)/32, inputSize(2)/32]);
bboxes = extractBoundingBoxes(yPred, anchors, numClasses, bboxParams, inputSize(1:2));
[bboxes, scores, labels] = selectStrongestBboxMulticlass(bboxes, reshape(yPred(numAnchors*(1:numClasses),:,:), [], 1), classNames);

% Display the detected objects and their labels.
I = insertObjectAnnotation(I, 'rectangle', bboxes, cellstr(labels));
imshow(I);

请注意，此示例代码是使用预训练的YOLOv2网络，该网络已在COCO数据集上进行了训练。您需要下载并加载 `yolov2DAG.mat` 模型文件和 `cocoClasses.txt` 类名文件，以便正确运行代码。此外，您需要将 `example.jpg` 图像文件替换为您自己的图像文件。