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In the past few years, mobile deep-learning deployment progressed by leapsand bounds, but solutions still struggle to accommodate its severe andfluctuating operational restrictions, which include bandwidth, latency,computation, and energy. In this work, we help to bridge that gap, introducingthe first configurable solution for object detection that manages the triplecommunication-computation-accuracy trade-off with a single set of weights. Oursolution shows state-of-the-art results on COCO-2017, adding only a minorpenalty on the base EfficientDet-D2 architecture. Our design is robust to thechoice of base architecture and compressor and should adapt well for futurearchitectures.

Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation