  Benchmarks, such as COCO, play a crucial role in object detection. However,
existing benchmarks are insufficient in scale variation, and their protocols
are inadequate for fair comparison. In this paper, we introduce the
Universal-Scale object detection Benchmark (USB). USB has variations in object
scales and image domains by incorporating COCO with the recently proposed Waymo
Open Dataset and Manga109-s dataset. To enable fair comparison, we propose USB
protocols by defining multiple thresholds for training epochs and evaluation
image resolutions. By analyzing methods on the proposed benchmark, we designed
fast and accurate object detectors called UniverseNets, which surpassed all
baselines on USB and achieved state-of-the-art results on existing benchmarks.
Specifically, UniverseNets achieved 54.1% AP on COCO test-dev with 20 epochs
training, the top result among single-stage detectors on the Waymo Open Dataset
Challenge 2020 2D detection, and the first place in the NightOwls Detection
Challenge 2020 all objects track. The code is available at
https://github.com/shinya7y/UniverseNet .
