  Object detectors are usually equipped with backbone networks designed for
image classification. It might be sub-optimal because of the gap between the
tasks of image classification and object detection. In this work, we present
DetNAS to use Neural Architecture Search (NAS) for the design of better
backbones for object detection. It is non-trivial because detection training
typically needs ImageNet pre-training while NAS systems require accuracies on
the target detection task as supervisory signals. Based on the technique of
one-shot supernet, which contains all possible networks in the search space, we
propose a framework for backbone search on object detection. We train the
supernet under the typical detector training schedule: ImageNet pre-training
and detection fine-tuning. Then, the architecture search is performed on the
trained supernet, using the detection task as the guidance. This framework
makes NAS on backbones very efficient. In experiments, we show the
effectiveness of DetNAS on various detectors, for instance, one-stage RetinaNet
and the two-stage FPN. We empirically find that networks searched on object
detection shows consistent superiority compared to those searched on ImageNet
classification. The resulting architecture achieves superior performance than
hand-crafted networks on COCO with much less FLOPs complexity.
