My goal is to train a pretrained mask rcnn on the TACO trash detection dataset of images. I want to match the annotations information with their corresponding images?

The data can be downloaded from Kaggle: https://www.kaggle.com/kneroma/tacotrashdataset

The code I was following can be found with this link in github: https://github.com/matlab-deep-learning/mask-rcnn

I want to train a pretrained maskr rcnn network on a trash dataset for detecting trash in the wild. So far I have this;

SetDir = fullfile('Training data');

Imds = imageDatastore(SetDir,'IncludeSubfolders',true,'LabelSource','foldernames');

annotationFile = jsondecode(fileread("annotations.json"));

save('Annotations.mat',"annotationFile")

%%

trainClassNames = {'Bottle', 'Can','Bottle cap'};

numClasses = length(trainClassNames);

imageSizeTrain = [800 800 3];

cocoAPIDir = fullfile("cocoapi-master","MatlabAPI");

addpath(cocoAPIDir);

unpackAnnotationDir = fullfile(SetDir,"annotations_unpacked","matFiles");

if ~exist(unpackAnnotationDir,'dir')

mkdir(unpackAnnotationDir)

end