What did you eat today? Can you develop a computational model smart enough to identify what you are eating? Automatic food identification can assist towards food intake monitoring to maintain a healthy diet. Food classification is a challenging problem due to the large number of food categories, high visual similarity between different food categories, as well as the lack of datasets that are large enough for training deep models. In this competition, we extend our last year's dataset to 251 fine-grained (prepared) food categories with 120,216 training images collected from the web. We provide human verified labels for both the validation set of 12,170 images and the test set of 28,399 images. The goal is to build a model to predict the fine-grained food-category label given an image.
Karan Sikka, SRI International
Parneet Kaur, Johnson and Johnson (work done while Parneet was an intern at SRI International)
Weijun Wang, Google
Ajay Divakaran, SRI International
Serge Belongie, Cornell University and Cornell Tech
We would like to the CVDF Foundation, and Tsung-Yi Lin for helping us with hosting the data. We would also like to thank SRI International and Google for support in data collection and labeling. The challenge is sponsored by SRI International.