% Train network options = trainingOptions('adam', 'Plots', 'training-progress'); net = trainNetwork(imdsTrain, layers, options);
% Load ground truth pixel labels imds = imageDatastore('images'); pxds = pixelLabelDatastore('labels', classNames, labelIDs); % Create U-Net lgraph = unetLayers([256 256 3], numClasses); % Train network options = trainingOptions('adam'
% Load pre-trained detector (requires Deep Learning Toolbox) detector = yolov2ObjectDetector('tiny-yolov2-coco'); % Read image I = imread('street_scene.jpg'); net = trainNetwork(imdsTrain
% Load pre-trained VDSR network net = vdsrNetwork; % Low-resolution image lrImage = imresize(highResImage, 0.25); lrImage = imresize(lrImage, size(highResImage)); pxds = pixelLabelDatastore('labels'
% Load and preprocess images imds = imageDatastore('image_folder', 'IncludeSubfolders', true, 'LabelSource', 'foldernames'); [imdsTrain, imdsValidation] = splitEachLabel(imds, 0.7, 'randomized'); % Define CNN architecture layers = [ imageInputLayer([64 64 3]) convolution2dLayer(3, 8, 'Padding', 'same') batchNormalizationLayer() reluLayer() maxPooling2dLayer(2, 'Stride', 2) fullyConnectedLayer(2) softmaxLayer() classificationLayer()];
% Predict pred = classify(net, imdsValidation); accuracy = mean(pred == imdsValidation.Labels); disp(['Accuracy: ', num2str(accuracy)]); Goal: Locate and classify multiple objects within an image.