Zusammenfassung
Comparing with enormous research achievements targeting better image
classification models, efforts applied to object detector training are dwarfed
in terms of popularity and universality. Due to significantly more complex
network structures and optimization targets, various training strategies and
pipelines are specifically designed for certain detection algorithms and no
other. In this work, we explore universal tweaks that help boosting the
performance of state-of-the-art object detection models to a new level without
sacrificing inference speed. Our experiments indicate that these freebies can
be as much as 5% absolute precision increase that everyone should consider
applying to object detection training to a certain degree.
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