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 creating 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 computer vision's effectiveness and high accuracy for dietary assessment.

翻译：如今，人们普遍会拍摄所食用的每一份饮料、零食或餐食，并将这些照片发布在社交媒体平台上。利用这些社会趋势，对捕获的食物图像进行实时识别和可靠分类，有望替代部分繁琐的食物日记记录与编码工作，从而支持个性化饮食干预。尽管中亚饮食在文化和历史上具有独特性，但该地区食品与饮食模式的公开数据仍十分匮乏。为填补这一空白，我们旨在创建一个易于公众消费者和研究者获取的区域性食品可靠数据集。据我们所知，这是首个中亚食品数据集（CAFD）构建工作。最终数据集包含42个食品类别和超过1.6万张该地区特有的民族菜肴图像。我们采用ResNet152神经网络模型，在CAFD上实现了88.70%的分类准确率（42类）。基于CAFD训练的食物识别模型展示了计算机视觉在膳食评估中的高效性与高精度。