AIcrowd: Food Recognition Challenge
Recognizing food from images is an extremely useful tool for a variety of use cases. In particular, it would allow people to track their food intake by simply taking a picture of what they consume. Food tracking can be of personal interest, and can often be of medical relevance as well. Medical studies have for some time been interested in the food intake of study participants, but had to rely on food frequency questionnaires that are known to be imprecise.
Image-based food recognition has in the past few years made substantial progress thanks to advances in deep learning. But food recognition remains a difficult problem for a variety of reasons.
The goal of this challenge is to train models which can look at images of food items and detect the individual food items present in them. We use a novel dataset of food images collected through the MyFoodRepo app where numerous volunteer Swiss users provide images of their daily food intake in the context of a digital cohort called Food & You. This growing data set has been annotated – or automatic annotations have been verified – with respect to segmentation, classification (mapping the individual food items onto an ontology of Swiss Food items), and weight / volume estimation.
This is an evolving dataset, where we will release more data as the dataset grows over time.