  Following the trends of mobile and edge computing for DNN models, an
intermediate option, split computing, has been attracting attentions from the
research community. Previous studies empirically showed that while mobile and
edge computing often would be the best options in terms of total inference
time, there are some scenarios where split computing methods can achieve
shorter inference time. All the proposed split computing approaches, however,
focus on image classification tasks, and most are assessed with small datasets
that are far from the practical scenarios. In this paper, we discuss the
challenges in developing split computing methods for powerful R-CNN object
detectors trained on a large dataset, COCO 2017. We extensively analyze the
object detectors in terms of layer-wise tensor size and model size, and show
that naive split computing methods would not reduce inference time. To the best
of our knowledge, this is the first study to inject small bottlenecks to such
object detectors and unveil the potential of a split computing approach. The
source code and trained models' weights used in this study are available at
https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors .
