MATLAB Answers

How to split training, validation and testing from ground truth file generated by ground truth label application

조회 수: 26(최근 30일)
Abdussalam Elhanashi
Abdussalam Elhanashi 19 May 2020
Hi guys
i want to know how to split training, validation and testing from ground truth file generated by ground truth labeler application i need for example 70% for training 20% for validation and 10% for testing
And then how can i evaluate the created detector with validation data and testing data using evaluateDetectionPrecision
Herein the code
load('gTruth.mat')
fruits = selectLabels(gTruth,{'apple','orange'});
if isfolder(fullfile('TrainingData'))
cd TrainingData
else
mkdir TrainingData
end
addpath('TrainingData');
options = trainingOptions('sgdm', ...
'MiniBatchSize', 32, ...
'InitialLearnRate', 1e-3, ...
'MaxEpochs', 250, ...
'CheckpointPath', tempdir);
classes = {'first', 'second'};
outputs = 1+numel(classes); % +1 for background class
layers = [
imageInputLayer([32 32 3],"Name","imageinput")
convolution2dLayer([3 3],32,"Name","conv_1","Padding",[1 1 1 1],"WeightsInitializer","narrow-normal")
reluLayer("Name","relu_1")
convolution2dLayer([3 3],32,"Name","conv_2","Padding",[1 1 1 1],"WeightsInitializer","narrow-normal")
reluLayer("Name","relu_2")
maxPooling2dLayer([2 2],"Name","maxpool","Stride",[2 2])
fullyConnectedLayer(64,"Name","fc_1","WeightsInitializer","narrow-normal")
reluLayer("Name","relu_3")
fullyConnectedLayer(outputs)
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];
trainingData = objectDetectorTrainingData(fruits,'SamplingFactor',1,...
'WriteLocation','TrainingData');
detector = trainFastRCNNObjectDetector(trainingData, layers, options, ...
'NegativeOverlapRange', [0 0.1], ...
'PositiveOverlapRange', [0.7 1]);
save('Detector.mat','detector');

답변(1개)

Uttiya Ghosh
Uttiya Ghosh 18 Jun 2020
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:
  댓글 수: 3

댓글을 달려면 로그인하십시오.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by