Object detectors are usually equipped with networks designed for image classification as backbones, e.g., ResNet. Although it is publicly known that there is a gap between the task of image classification and object detection, designing a suitable detector backbone is still manually exhaustive. In this paper, we propose DetNAS to automatically search neural architectures for the backbones of object detectors. In DetNAS, the search space is formulated into a supernet and the search method relies on evolution algorithm (EA). In experiments, we show the effectiveness of DetNAS on various detectors, the one-stage detector, RetinaNet, and the two-stage detector, FPN. For each case, we search in both training from scratch scheme and ImageNet pre-training scheme. There is a consistent superiority compared to the architectures searched on ImageNet classification. Our main result architecture achieves better performance than ResNet-101 on COCO with the FPN detector. In addition, we illustrate the architectures searched by DetNAS and find some meaningful patterns.

翻译：在本文中,我们建议DetNAS自动搜索物体探测器的神经元结构。在DetNAS中,搜索空间形成一个超级网,搜索方法依赖于演化算法(EA)。在实验中,我们展示了DetNAS在各种探测器、单级探测器、RetinaNet和两阶段探测器FPN上的有效性。我们在每一个案例中都从抓图方案和图像网络预培训计划中进行搜索。与图像网络分类所搜索的建筑相比,我们的主要结果结构的性能优于FPN探测器的COCO-101。此外,我们展示了DetNAS所搜索的建筑,并发现了一些有意义的模式。