  With the rapid development of electronic commerce, the way of shopping has
experienced a revolutionary evolution. To fully meet customers' massive and
diverse online shopping needs with quick response, the retailing AI system
needs to automatically recognize products from images and videos at the
stock-keeping unit (SKU) level with high accuracy. However, product recognition
is still a challenging task, since many of SKU-level products are fine-grained
and visually similar by a rough glimpse. Although there are already some
products benchmarks available, these datasets are either too small (limited
number of products) or noisy-labeled (lack of human labeling). In this paper,
we construct a human-labeled product image dataset named "Products-10K", which
contains 10,000 fine-grained SKU-level products frequently bought by online
customers in JD.com. Based on our new database, we also introduced several
useful tips and tricks for fine-grained product recognition. The products-10K
dataset is available via https://products-10k.github.io/.
