The Food Recognition Benchmark: Using DeepLearning to Recognize Food on
  Images
  The automatic recognition of food on images has numerous interesting
applications, including nutritional tracking in medical cohorts. The problem
has received significant research attention, but an ongoing public benchmark to
develop open and reproducible algorithms has been missing. Here, we report on
the setup of such a benchmark using publicly available food images sourced
through the mobile MyFoodRepo app. Through four rounds, the benchmark released
the MyFoodRepo-273 dataset constituting 24,119 images and a total of 39,325
segmented polygons categorized in 273 different classes. Models were evaluated
on private tests sets from the same platform with 5,000 images and 7,865
annotations in the final round. Top-performing models on the 273 food
categories reached a mean average precision of 0.568 (round 4) and a mean
average recall of 0.885 (round 3). We present experimental validation of round
4 results, and discuss implications of the benchmark setup designed to increase
the size and diversity of the dataset for future rounds.
