How can I import ground truth data into Matlab for the training of a (faster) R-CNN?

Question

I have a large, labelled, dataset which I have created and I would like to provide it to Matlab to train an R-CNN (using the faster R-CNN algorithm). How can this be done?

The built-in labeller provided by Matlab requires that the user manually load each data sample and label it with a graphical user interface. This is not practical for me as the set is already labelled and it contains 500,000 samples.

It should be noted, that I can control the format in which the data set is stored. So, I can create .csv files or excel files if needed.

I have tried two directions: 1. Creating a mat file, similar to the one created by the labeller. 2. Looked for ways within Matlab to import the data from .csv or excel files.

I have had no success with either methods.

For Direction 1:

Though there are many libraries that can open mat files, they are not able to open or create files similar to the Matlab ground truths because these are not simple matrices (the cells themselves contain matrices of varying dimensions that represent the bounding boxes of each classified object). Moreover, though the Matlab Level 5 file format is open source I have not been successful in using it to write my own code (C# or C++) to parse and write such files.

For Direction 2:

There are generic methods in Matlab to load .csv and excel files but I do not know how to organize these files in such a way as to produce the structure that the labeller creates and that is consumed by the fasterRCNN trainer.