iFood 2019


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.

Competition Details

Checkout the iFood Competition Github repo for the specifics of the dataset and download links. The competition itself is being hosted on Kaggle.

Competition Begins 22 April 2019

Submission Deadline 3 June 2019


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.