Paper Info
Title
DetNAS: Backbone Search for Object Detection
Abstract
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. Code and models have been made available at: https://github.com/megvii-model/DetNAS.
Year
Venue
Keywords
2019
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
image classification,object detection
Field
DocType
Volume
Computer vision,Object detection,Computer science,Artificial intelligence,Machine learning
Conference
32
ISSN
Citations 
PageRank 
1049-5258
4
0.39
References 
Authors
0
6
Name
Order
Citations
PageRank
Yukang Chen1163.89
Tong Yang2263.40
Xiangyu Zhang313044437.66
Gaofeng Meng454335.45
Xinyu Xiao5122.31
Jian Sun625842956.90