Hi Abdussalam,
From my understanding, you would like to know the function that can be used to split a dataset into training, validation and testing sub datasets. I am assuming a random dataset of size 1000*10 and providing you the solution below.
% Define the datasize
dataSize=1000;
featureDim=10;
% Create the dataset
data=rand(dataSize,featureDim);
% Define the split sizes
trainSize=0.7*dataSize;
valSize=0.2*dataSize;
testSize=0.1*dataSize;
% Create the training data
cv=cvpartition(dataSize,'HoldOut',valSize+testSize);
idx=cv.test;
dataTrain = data(~idx,:);
dataValTest = data(idx,:);
% Create validation and test data
cv=cvpartition(valSize+testSize,'HoldOut',testSize);
idx=cv.test;
dataVal = data(~idx,:);
dataTest = data(idx,:);
For the second part of your question, from my undersanding you want to know the usage of usage of evaluateDetectionPrecision. I have used 'vehicleTrainingData.mat' file to explain the usage, This file is available in MATLAB.
% Load vehicleTrainingData
vehicleData = load('vehicleTrainingData.mat');
trainingData = data.vehicleTrainingData;
% Add fullpath to the local vehicle data folder.
dataDir = fullfile(toolboxdir('vision'), 'visiondata');
trainingData.imageFilename = fullfile(dataDir, trainingData.imageFilename);
% Create an imageDatastore using the files from the table
imds = imageDatastore(trainingData.imageFilename);
% Create a boxLabelDatastore using the label columns from the table
blds = boxLabelDatastore(trainingData(:,2:end));
% Load YOLOv2 Detector
vehicleDetector = load('yolov2VehicleDetector.mat');
detector = vehicleDetector.detector;
% Run the detector with imageDatastore.
results = detect(detector, imds);
% Evaluate the results against the ground truth data.
[ap, recall, precision] = evaluateDetectionPrecision(results, blds);
For more information, refer to the links given below:
