适于行人重识别的二分支EfficientNet网络设计

Design of A Two-Branch EfficientNet for Person Re-Identification

  • 摘要: 鉴于ResNet的强大表达能力,其在行人重识别领域获得了广泛的应用。虽然基于ResNet50构建的行人重识别网络取得了优异的性能,但流行的ResNet50仍存在模型体积大、效率低等局限性。与之相比,EfficientNet作为一种新兴的深度模型,具有设计合理、运行高效等特点,并在ImageNet数据集上有着更出色的性能表现。为此,本文尝试将EfficientNet系列网络引入到行人重识别领域,替代比较流行的ResNet50主干网络,提供了一个全新的骨干网基线。本文重点根据EfficientNet系列网络给出一种二分支行人重识别网络构造。相比于ResNet50,基于EfficientNet构造的二分支行人重识别网络具有网络参数规模小、性能提升明显的特点。实验结果表明:所构造的网络在行人重识别流行数据集上均有良好的表现。

     

    Abstract: Due to its strong expressive ability, ResNet is widely used in Person Re-Identification. Although the use of ResNet50 for person ReID has achieved excellent performance, the popular ResNet-50-based solution consumes considerable resources with low efficiency in implementations. Recently, EfficientNet was proposed as an emerging alternative for design of deep neural models with much better efficiency. In this paper, we propose to design a two-branch EfficientNet for person ReID. Compared with various ResNet-50-based solutions, the proposed EfficientNet has a significantly small size on network, but often achieves better performance. Experimental results show that the proposed network performs very well on the popular Person Re-Identification data set.

     

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