DetNAS: Backbone Search for Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian, Sun

TL;DR
This paper introduces DetNAS, a neural architecture search framework tailored for designing backbone networks optimized for object detection, outperforming traditional classification-based backbones on COCO with fewer FLOPs.
Contribution
DetNAS presents a novel NAS framework that efficiently searches for object detection-specific backbones using a supernet trained with detection tasks, bridging the gap between classification and detection.
Findings
Searched architectures outperform classification-based backbones on COCO.
Detection-specific backbones achieve higher accuracy with less FLOPs.
Framework is effective for both one-stage and two-stage detectors.
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.…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Domain Adaptation and Few-Shot Learning
MethodsDepthwise Convolution · Channel Shuffle · ShuffleNet V2 Block · Batch Normalization · *Communicated@Fast*How Do I Communicate to Expedia? · DetNASNet · Focal Loss · Convolution · 1x1 Convolution · Weight Decay
