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Nowadays, it is common for people to take photographs of every beverage,snack, or meal they eat and then post these photographs on social mediaplatforms. Leveraging these social trends, real-time food recognition andreliable classification of these captured food images can potentially helpreplace some of the tedious recording and coding of food diaries to enablepersonalized dietary interventions. Although Central Asian cuisine isculturally and historically distinct, there has been little published data onthe food and dietary habits of people in this region. To fill this gap, we aimto create a reliable dataset of regional foods that is easily accessible toboth public consumers and researchers. To the best of our knowledge, this isthe first work on creating a Central Asian Food Dataset (CAFD). The finaldataset contains 42 food categories and over 16,000 images of national dishesunique to this region. We achieved a classification accuracy of 88.70\% (42classes) on the CAFD using the ResNet152 neural network model. The foodrecognition models trained on the CAFD demonstrate computer vision'seffectiveness and high accuracy for dietary assessment.

A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract