Running Tensorflow Object Detection API from scratch (no finetuning – with randomly initialised model)

It’s standard practice to finetune an object detection model for
a given task. Finetuning is part of the workflow of the
Tensorflow Object Detection workflow tutorial.

However, I have been tasked by a sceptical supervisor to show
that using a pretrained model actually improves performance. So I
need a way to reinitialise the parameters of
one of the pretrained TF Object detection models, so I can
train and convince the supervisor that finetuning is actually best
practice.

However, I haven’t found a way to do this – finetuning seems to
be baked in. Is there a way I can reinitalise the weights of the
network, following the Tensorflow Object Detection workflow
tutorial?