Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue that encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. SpineNet achieves state-of-the-art performance of one-stage object detector on COCO with 60% less computation, and outperforms ResNet-FPN counterparts by 6% AP. SpineNet architecture can transfer to classification tasks, achieving 6% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset.

本文提出了SpineNet，这是一个具有尺度置换中间特征和跨尺度连接的主干网络，在神经架构搜索的目标检测任务中学习。它在各种尺度上使用类似的构建块进行建模，而使用的FLOPs较少。在各种检测器上，SpineNet-190单模型获得了远高于之前检测器的52.5% AP结果，并且转移能力良好，在分类任务中表现出色，相较之前还获得了5%的上升。

SpineNet：学习尺度置换骨干网络以进行识别和定位