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.

翻译：在本文中,我们想要展示深神经网络(DNN)范围内动态自动调控过程的动态自动调控方法的潜在好处,以应对物体探测挑战。我们测试了不同的神经网络,为众所周知的COCO 17数据库寻找最佳检测器,我们证明即使我们只考虑预测质量,没有单一的最佳网络。如果我们也考虑用解决方案的时间作为衡量尺度来评估,然后选择最合适的网络,这一点就更加明显了。这打开了一种适应性方法的可能性,在不同物体探测网络之间根据运行时间要求转换(例如,受时间到溶解限制的最大质量)。此外,我们通过开发一个特别的或触角,我们证明额外的主动方法可以带来更大的好处,使我们能够根据所处理图像的某些特点选择现有的最佳网络。为了利用这种方法,我们需要确定一些能够用来指导最有希望网络决策的图像特征。尽管已经确定了优化机会,但我们无法确定一个预测器功能,也没有通过采用一个古典图像来验证这一模型。