通过一次性神经结构搜索设计目标检测网络

Chuntung Zhuang
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摘要

以往的NAS主要集中在图像分类上,因此直接实现传统的NAS,如最后的研究工作DetNet,在目标检测任务上是无效的。一般来说,传统的NAS只搜索骨干网架构,而完全忽略了头部网络。与图像分类任务可以直接在ImageNet等分类数据集上执行NAS不同,目标检测任务需要在分类和检测数据集上进行多次迭代训练。此外,在对象检测任务上使用传统的NAS方法是计算密集型的(超过数百个GPU小时),因为NAS通常有两个阶段的工作流程。从超级网络衍生出来的最佳子网络架构必须经过重新训练或微调。为了解决上述挑战,我们提出了一种针对目标检测任务的新的NAS方法DetNAS。首先,DetNAS可以加速神经网络架构的搜索过程,以满足目标检测任务的各种需求。DetNet只在骨干网中搜索目标检测任务,而DetNAS在网络架构搜索时可以同时搜索骨干网和头网。同时,受前人工作的启发,DetNAS用渐进式搜索取代了DetNet中的单路径进化算法,进一步提高了网络结构的搜索效率。其次,先前的研究提出了一系列提高基于图像分类的网络架构搜索效率的技术,但它们对目标检测任务的影响仍然未知。为了彻底验证这些结论的有效性,我们在多个数据集和不同的实验设置上进行了烧蚀实验,为后续的研究工作提供了有价值的基础和参考。据我们所知,DetNAS是第一个可以同时搜索骨干和头网的一次性NAS方法。我们相信我们的工作将为探索目标检测模型的架构开辟一个新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DetNAS: Design Object Detection Network via One-Shot Neural Architecture Search
Previous NAS focus on image classification, so directly implementing traditional NAS, like the last research work DetNet, on object detection tasks are ineffective. In general, conventional NAS only searches the backbone network architecture while completely ignore the head network. Unlike image classification tasks, which can directly perform NAS on classification datasets such as ImageNet, object detection tasks require iterative training on classification and detection datasets multiple times. In addition, using traditional NAS methods on object detection tasks is computation-intensive (more than hundreds of GPU hours) because NAS typically has a two-stage workflow. The best sub-Network architecture derived from the super-Network must be retrained or fine-tuned. To resolve the above challenges, we propose DetNAS, a new NAS method targeting object detection tasks. First of all, DetNAS can accelerate the search process of neural network architecture to meet the various demands of object detection tasks. DetNet only searches the backbone network for object detection tasks, while DetNAS can simultaneously search for the backbone and head network during network architecture search. At the same time, inspired by the previous work, DetNAS replaced the single-path evolutionary algorithm in DetNet with progressive search, which further improved the search efficiency of the network structure. Secondly, previous research suggested a series of techniques to improve image classification-based network architecture search efficiency, but their effects on object detection tasks are still unknown. To thoroughly verify the validity of these conclusions, we conducted ablation experiments on multiple datasets and various experimental settings, which provided a valuable basis and reference for subsequent research work. As far as we know, DetNAS is the first One-shot NAS method that can search the backbone and head network simultaneously. We believe our work will open up a new direction to explore the architecture of object detection models.
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