UNIMIB Food Database

Health care on food and good practices in dietary behavior are drawing people’s attention recently. Nowadays technology can support the users in keep tracks of their food consumption, and to increase the awareness in their daily diet by monitoring their food habits. In the recent years many research works have demonstrated that computer vision techniques can help to automatically recognize diverse foods and to estimate the food quantity. Both these two goals are fundamental for a comprehensive diet monitoring system.We have designed datasets and algorithms for automatic dietary monitoring of canteen customers based on robust computer vision techniques.2016 versionThis database can be used for food recognition. The database is composed of 1,027 tray images with multiple foods and containing 73 food categories.2015 versionThis database can be used for food recognition and leftoevr estimation. Used in our paper “” where we built a complete system for food logging in a canteen environment. The database is composed of 2,000 tray images with multiple foods and containing 15 food categories. The images are paired with the corresponding empy trays that can be used for leftover estimation.