  Training CNN for detection is time-consuming due to the large dataset and
complex network modules, making it hard to search architectures on detection
datasets directly, which usually requires vast search costs (usually tens and
even hundreds of GPU-days). In contrast, this paper introduces an efficient
framework, named EAutoDet, that can discover practical backbone and FPN
architectures for object detection in 1.4 GPU-days. Specifically, we construct
a supernet for both backbone and FPN modules and adopt the differentiable
method. To reduce the GPU memory requirement and computational cost, we propose
a kernel reusing technique by sharing the weights of candidate operations on
one edge and consolidating them into one convolution. A dynamic channel
refinement strategy is also introduced to search channel numbers. Extensive
experiments show significant efficacy and efficiency of our method. In
particular, the discovered architectures surpass state-of-the-art object
detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3
FPS on COCO test-dev set. We also transfer the discovered architectures to
rotation detection task, which achieve 77.05 mAP$_{\text{50}}$ on DOTA-v1.0
test set with 21.1M parameters.
