  Neural Architecture Search (NAS) has achieved great success in image
classification task. Some recent works have managed to explore the automatic
design of efficient backbone or feature fusion layer for object detection.
However, these methods focus on searching only one certain component of object
detector while leaving others manually designed. We identify the inconsistency
between searched component and manually designed ones would withhold the
detector of stronger performance. To this end, we propose a hierarchical
trinity search framework to simultaneously discover efficient architectures for
all components (i.e. backbone, neck, and head) of object detector in an
end-to-end manner. In addition, we empirically reveal that different parts of
the detector prefer different operators. Motivated by this, we employ a novel
scheme to automatically screen different sub search spaces for different
components so as to perform the end-to-end search for each component on the
corresponding sub search space efficiently. Without bells and whistles, our
searched architecture, namely Hit-Detector, achieves 41.4\% mAP on COCO minival
set with 27M parameters. Our implementation is available at
https://github.com/ggjy/HitDet.pytorch.
