  With the increasing performance of machine learning techniques in the last
few years, the computer vision and robotics communities have created a large
number of datasets for benchmarking object recognition tasks. These datasets
cover a large spectrum of natural images and object categories, making them not
only useful as a testbed for comparing machine learning approaches, but also a
great resource for bootstrapping different domain-specific perception and
robotic systems. One such domain is domestic environments, where an autonomous
robot has to recognize a large variety of everyday objects such as groceries.
This is a challenging task due to the large variety of objects and products,
and where there is great need for real-world training data that goes beyond
product images available online. In this paper, we address this issue and
present a dataset consisting of 5,000 images covering 25 different classes of
groceries, with at least 97 images per class. We collected all images from
real-world settings at different stores and apartments. In contrast to existing
groceries datasets, our dataset includes a large variety of perspectives,
lighting conditions, and degrees of clutter. Overall, our images contain
thousands of different object instances. It is our hope that machine learning
and robotics researchers find this dataset of use for training, testing, and
bootstrapping their approaches. As a baseline classifier to facilitate
comparison, we re-trained the CaffeNet architecture (an adaptation of the
well-known AlexNet) on our dataset and achieved a mean accuracy of 78.9%. We
release this trained model along with the code and data splits we used in our
experiments.
