  This work is for designing one-stage lightweight detectors which perform well
in terms of mAP and latency. With baseline models each of which targets on GPU
and CPU respectively, various operations are applied instead of the main
operations in backbone networks of baseline models. In addition to experiments
about backbone networks and operations, several feature pyramid network (FPN)
architectures are investigated. Benchmarks and proposed detectors are analyzed
in terms of the number of parameters, Gflops, GPU latency, CPU latency and mAP,
on MS COCO dataset which is a benchmark dataset in object detection. This work
propose similar or better network architectures considering the trade-off
between accuracy and latency. For example, our proposed GPU-target backbone
network outperforms that of YOLOX-tiny which is selected as the benchmark by
1.43x in speed and 0.5 mAP in accuracy on NVIDIA GeForce RTX 2080 Ti GPU.
