A Central Asian Food Dataset for Personalized Dietary Interventions,
  Extended Abstract
Aknur Karabay Arman Bolatov Huseyin Atakan Varol Mei-Yen Chan
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.