iFood 2018

Being able to automatically identify the food items in an image 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 introduce a new dataset of 211 fine-grained (prepared) food categories with 101733 training images collected from the web. We provide human verified labels for both the validation set of 10323 images and the test set of 24088 images. The goal is to build a model to predict the fine-grained food-category label given an image.

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 April 2018

Submission Deadline 15th June 2018

Karan Sikka, SRI International

Parneet Kaur*, Johnson and Johnson

Weijun Wang, Google

Ajay Divakaran, SRI International

Serge Belongie, Cornell University and Cornell Tech

*work done while Parneet was an intern at SRI International

We would like to the CVDF Foundation, and Tsung-Yi Lin for helping us with hosting the data.