  Image classification models built into visual support systems and other
assistive devices need to provide accurate predictions about their environment.
We focus on an application of assistive technology for people with visual
impairments, for daily activities such as shopping or cooking. In this paper,
we provide a new benchmark dataset for a challenging task in this application -
classification of fruits, vegetables, and refrigerated products, e.g. milk
packages and juice cartons, in grocery stores. To enable the learning process
to utilize multiple sources of structured information, this dataset not only
contains a large volume of natural images but also includes the corresponding
information of the product from an online shopping website. Such information
encompasses the hierarchical structure of the object classes, as well as an
iconic image of each type of object. This dataset can be used to train and
evaluate image classification models for helping visually impaired people in
natural environments. Additionally, we provide benchmark results evaluated on
pretrained convolutional neural networks often used for image understanding
purposes, and also a multi-view variational autoencoder, which is capable of
utilizing the rich product information in the dataset.
