  We propose an approach to Multitask Learning (MTL) to make deep learning
models faster and lighter for applications in which multiple tasks need to be
solved simultaneously, which is particularly useful in embedded, real-time
systems. We develop a multitask model for both Object Detection and Semantic
Segmentation and analyze the challenges that appear during its training. Our
multitask network is 1.6x faster, lighter and uses less memory than deploying
the single-task models in parallel. We conclude that MTL has the potential to
give superior performance in exchange of a more complex training process that
introduces challenges not present in single-task models.
