  Food classification is a challenging problem due to the large number of
categories, high visual similarity between different foods, as well as the lack
of datasets for training state-of-the-art deep models. Solving this problem
will require advances in both computer vision models as well as datasets for
evaluating these models. In this paper we focus on the second aspect and
introduce FoodX-251, a dataset of 251 fine-grained food categories with 158k
images collected from the web. We use 118k images as a training set and provide
human verified labels for 40k images that can be used for validation and
testing. In this work, we outline the procedure of creating this dataset and
provide relevant baselines with deep learning models. The FoodX-251 dataset has
been used for organizing iFood-2019 challenge in the Fine-Grained Visual
Categorization workshop (FGVC6 at CVPR 2019) and is available for download.
