Recently, Neural Architecture Search has achieved great success in
large-scale image classification. In contrast, there have been limited works
focusing on architecture search for object detection, mainly because the costly
ImageNet pre-training is always required for detectors. Training from scratch,
as a substitute, demands more epochs to converge and brings no computation
saving. To overcome this obstacle, we introduce a practical neural architecture
transformation search(NATS)algorithm for object detection in this paper.
Instead of searching and constructing an entire network, NATS explores the
architecture space on the base of existing network and reusing its weights. We
propose a novel neural architecture search strategy in channel-level instead of
path-level and devise a search space specially targeting at object detection.
With the combination of these two designs, an architecture transformation
scheme could be discovered to adapt a network designed for image classification
to task of object detection. Since our method is gradient-based and only
searches for a transformation scheme, the weights of models pretrained
inImageNet could be utilized in both searching and retraining stage, which
makes the whole process very efficient. The transformed network requires no
extra parameters and FLOPs, and is friendly to hardware optimization, which is
practical to use in real-time application. In experiments, we demonstrate the
effectiveness of NATSon networks like ResNet and ResNeXt. Our transformed
networks, combined with various detection frameworks, achieve significant
improvements on the COCO dataset while keeping fast.

本文介绍了一种新的基于梯度的神经架构变换搜索算法 (NATS)，它可以将以图像分类为目的设计的神经网络重新构建为目标检测网络，而且不需要额外的参数和计算量，“transformed networks” 在 COCO 数据集上表现显著提高，适用于实时应用。