Recently, considerable research efforts have been devoted to food recognition for its great potential applications in human health. Much work so far has focused on directly extracted deep visual features via Convolutional Neural Networks, which require significant computational resources and training time. The high requirements on hardware resources severely limit the application of food recognition in mobile devices and the sustainable extension on the server side. Therefore, how to design an efficient and high-performance lightweight neural network for food recognition is the key to solve the problem. In this paper, we propose a Lightweight Transformer-Based Deep Neural Network for food image recognition, which can achieve effective recognition of food images with fewer parameters and lower computational cost. Through Transformer Grouping and Token Shuffling, we construct an efficient food image recognition network that effectively combines the advantages of Transformer to extract global features and MobileNet to extract local features. The proposed network architecture effectively copes with the particularly scattered distribution of salient features in food images, and improves the recognition rate. We conduct extensive experiments on three popular food data sets, demonstrating that our method achieves state-of-the-art performance in applying lightweight neural networks to food image recognition.

中文翻译：

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通过具有变换器分组的高效神经网络进行食物识别

最近，大量研究工作致力于食品识别，因为它在人类健康方面具有巨大的潜在应用。到目前为止，很多工作都集中在通过卷积神经网络直接提取深度视觉特征，这需要大量的计算资源和训练时间。对硬件资源的高要求严重限制了食品识别在移动设备上的应用和在服务器端的可持续扩展。因此，如何设计一个高效、高性能的轻量级食物识别神经网络是解决问题的关键。在本文中，我们提出了一种基于轻量级 Transformer 的深度神经网络用于食物图像识别，它可以以更少的参数和更低的计算成本实现对食物图像的有效识别。通过 Transformer Grouping 和 Token Shuffling，我们构建了一个高效的食物图像识别网络，有效地结合了 Transformer 提取全局特征和 MobileNet 提取局部特征的优点。所提出的网络架构有效地应对了食物图像中显着特征分布特别分散的问题，提高了识别率。我们对三个流行的食物数据集进行了大量实验，证明我们的方法在将轻量级神经网络应用于食物图像识别方面实现了最先进的性能。所提出的网络架构有效地应对了食物图像中显着特征分布特别分散的问题，提高了识别率。我们对三个流行的食物数据集进行了大量实验，证明我们的方法在将轻量级神经网络应用于食物图像识别方面实现了最先进的性能。所提出的网络架构有效地应对了食物图像中显着特征分布特别分散的问题，提高了识别率。我们对三个流行的食物数据集进行了大量实验，证明我们的方法在将轻量级神经网络应用于食物图像识别方面实现了最先进的性能。