  In this paper, we want to show the potential benefit of a dynamic auto-tuning
approach for the inference process in the Deep Neural Network (DNN) context,
tackling the object detection challenge. We benchmarked different neural
networks to find the optimal detector for the well-known COCO 17 database, and
we demonstrate that even if we only consider the quality of the prediction
there is not a single optimal network. This is even more evident if we also
consider the time to solution as a metric to evaluate, and then select, the
most suitable network. This opens to the possibility for an adaptive
methodology to switch among different object detection networks according to
run-time requirements (e.g. maximum quality subject to a time-to-solution
constraint).
  Moreover, we demonstrated by developing an ad hoc oracle, that an additional
proactive methodology could provide even greater benefits, allowing us to
select the best network among the available ones given some characteristics of
the processed image. To exploit this method, we need to identify some image
features that can be used to steer the decision on the most promising network.
Despite the optimization opportunity that has been identified, we were not able
to identify a predictor function that validates this attempt neither adopting
classical image features nor by using a DNN classifier.
