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.
Organizers and Acknowledgements
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.