Task (project management)

Engineering

Artificial intelligence

Object detection

Combinatorics

Pattern recognition (psychology)

Mathematics

Backbone network

Morphism

Systems engineering

Geography

Parallel computing

Computer science

Geodesy

Benchmark (surveying)

Computer network

Advanced object detectors usually adopt a backbone network designed and pretrained by ImageNet classification. Recently neural architecture search (NAS) has emerged to automatically design a task-specific backbone to bridge the gap between the tasks of classification and detection. In this paper, we propose a two-phase serial-to-parallel architecture search framework named SP-NAS towards a flexible task-oriented detection backbone. Specifically, the serial-searching round aims at finding a sequence of serial blocks with optimal scale and output channels in the feature hierarchy by a Swap-Expand-Reignite search algorithm; the parallel-searching phase then assembles several sub-architectures along with the previous searched backbone into a more powerful parallel-structured backbone. We efficiently search a detection backbone by exploring a network morphism strategy on multiple detection benchmarks. The resulting architectures achieve SOTA results, i.e. top performance (LAMR: 0.055) on the automotive detection leaderboard of EuroCityPersons benchmark, improving 2.3% mAP with less FLOPS than NAS-FPN on COCO, and reaching 84.1% AP50 on VOC better than DetNAS and Auto-FPN in terms of both accuracy and speed.

SP-NAS: Serial-to-Parallel Backbone Search for Object Detection