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基于深度卷积网络的地中海食物图像识别

Mediterranean Food Image Recognition Using Deep Convolutional Networks.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1740-1743. doi: 10.1109/EMBC46164.2021.9630481.

Abstract

We present a new dataset of food images that can be used to evaluate food recognition systems and dietary assessment systems. The Mediterranean Greek food -MedGRFood dataset consists of food images from the Mediterranean cuisine, and mainly from the Greek cuisine. The dataset contains 42,880 food images belonging to 132 food classes which have been collected from the web. Based on the EfficientNet family of convolutional neural networks, specifically the EfficientNetB2, we propose a new deep learning schema that achieves 83.4% top-1 accuracy and 97.8% top-5 accuracy in the MedGRFood dataset for food recognition. This schema includes the use of the fine tuning, transfer learning and data augmentation technique.

摘要

我们提出了一个新的食物图像数据集,可用于评估食物识别系统和饮食评估系统。Mediterranean Greek food -MedGRFood 数据集由来自地中海美食的食物图像组成,主要来自希腊美食。该数据集包含 42880 张食物图像,属于 132 个食物类别,这些图像是从网络上收集的。基于卷积神经网络的 EfficientNet 系列,特别是 EfficientNetB2,我们提出了一种新的深度学习方案,在 MedGRFood 数据集的食物识别中实现了 83.4%的 top-1 准确率和 97.8%的 top-5 准确率。该方案包括使用微调、迁移学习和数据增强技术。

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