  Scale-wise evaluation of object detectors is important for real-world
applications. However, existing metrics are either coarse or not sufficiently
reliable. In this paper, we propose novel scale-wise metrics that strike a
balance between fineness and reliability, using a filter bank consisting of
triangular and trapezoidal band-pass filters. We conduct experiments with two
methods on two datasets and show that the proposed metrics can highlight the
differences between the methods and between the datasets. Code is available at
https://github.com/shinya7y/UniverseNet .
