  In deep learning area, large-scale image datasets bring a breakthrough in the
success of object recognition and retrieval. Nowadays, as the embodiment of
innovation, the diversity of the industrial goods is significantly larger, in
which the incomplete multiview, multimodal and multilabel are different from
the traditional dataset. In this paper, we introduce an industrial goods
dataset, namely PatentNet, with numerous highly diverse, accurate and detailed
annotations of industrial goods images, and corresponding texts. In PatentNet,
the images and texts are sourced from design patent. Within over 6M images and
corresponding texts of industrial goods labeled manually checked by
professionals, PatentNet is the first ongoing industrial goods image database
whose varieties are wider than industrial goods datasets used previously for
benchmarking. PatentNet organizes millions of images into 32 classes and 219
subclasses based on the Locarno Classification Agreement. Through extensive
experiments on image classification, image retrieval and incomplete multiview
clustering, we demonstrate that our PatentNet is much more diverse, complex,
and challenging, enjoying higher potentials than existing industrial image
datasets. Furthermore, the characteristics of incomplete multiview, multimodal
and multilabel in PatentNet are able to offer unparalleled opportunities in the
artificial intelligence community and beyond.
