  Maintaining a healthy lifestyle has become increasingly challenging in
today's sedentary society marked by poor eating habits. To address this issue,
both national and international organisations have made numerous efforts to
promote healthier diets and increased physical activity. However, implementing
these recommendations in daily life can be difficult, as they are often generic
and not tailored to individuals. This study presents the AI4Food-NutritionDB
database, the first nutrition database that incorporates food images and a
nutrition taxonomy based on recommendations by national and international
health authorities. The database offers a multi-level categorisation,
comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73
subcategories (e.g., "White Meat"), and 893 specific food products (e.g.,
"Chicken"). The AI4Food-NutritionDB opens the doors to new food computing
approaches in terms of food intake frequency, quality, and categorisation.
Also, we present a standardised experimental protocol and benchmark including
three tasks based on the nutrition taxonomy (i.e., category, subcategory, and
final product recognition). These resources are available to the research
community, including our deep learning models trained on AI4Food-NutritionDB,
which can serve as pre-trained models, achieving accurate recognition results
for challenging food image databases.
