Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan.
School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan.
Nutrients. 2023 Mar 31;15(7):1728. doi: 10.3390/nu15071728.
Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.
如今，人们在吃喝任何东西时都会拍照，并将这些照片发布到社交媒体平台上，这种现象十分普遍。利用这些社交媒体趋势，实时的食物识别和对这些捕获的食物图像的可靠分类，可以帮助代替一些繁琐的食物日记记录和编码，从而实现个性化的饮食干预。尽管中亚美食在文化和历史上具有独特性，但关于该地区人们的饮食和饮食习惯的已发表数据却很少。为了填补这一空白，我们旨在创建一个可靠的区域性食品数据集，使公众消费者和研究人员都能轻松访问该数据集。据我们所知，这是首个关于创建中亚食品数据集（CAFD）的工作。最终数据集包含 42 个食品类别和超过 16000 张该地区特有的国菜图像。我们使用 ResNet152 神经网络模型在 CAFD 上实现了 88.70%（42 类）的分类准确率。在 CAFD 上训练的食物识别模型证明了计算机视觉在饮食评估方面的有效性和高精度。
