  Over recent years, emerging interest has occurred in integrating computer
vision technology into the retail industry. Automatic checkout (ACO) is one of
the critical problems in this area which aims to automatically generate the
shopping list from the images of the products to purchase. The main challenge
of this problem comes from the large scale and the fine-grained nature of the
product categories as well as the difficulty for collecting training images
that reflect the realistic checkout scenarios due to continuous update of the
products. Despite its significant practical and research value, this problem is
not extensively studied in the computer vision community, largely due to the
lack of a high-quality dataset. To fill this gap, in this work we propose a new
dataset to facilitate relevant research. Our dataset enjoys the following
characteristics: (1) It is by far the largest dataset in terms of both product
image quantity and product categories. (2) It includes single-product images
taken in a controlled environment and multi-product images taken by the
checkout system. (3) It provides different levels of annotations for the
check-out images. Comparing with the existing datasets, ours is closer to the
realistic setting and can derive a variety of research problems. Besides the
dataset, we also benchmark the performance on this dataset with various
approaches. The dataset and related resources can be found at
\url{https://rpc-dataset.github.io/}.
