FoodX-251: A Dataset for Fine-grained Food Classification

TL;DR

FoodX-251 is a comprehensive dataset with 158,000 images across 251 fine-grained food categories, designed to advance food classification research by providing high-quality, human-verified labels and supporting deep learning model development.

Contribution

This paper introduces FoodX-251, a new large-scale, fine-grained food dataset with verified labels, addressing the lack of suitable datasets for food classification tasks.

Findings

Baseline deep learning models evaluated on FoodX-251.

The dataset facilitated the organization of the iFood-2019 challenge.

FoodX-251 is publicly available for research use.

Abstract

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.