  Intellectual property protection(IPP) have received more and more attention
recently due to the development of the global e-commerce platforms. brand
recognition plays a significant role in IPP. Recent studies for brand
recognition and detection are based on small-scale datasets that are not
comprehensive enough when exploring emerging deep learning techniques.
Moreover, it is challenging to evaluate the true performance of brand detection
methods in realistic and open scenes. In order to tackle these problems, we
first define the special issues of brand detection and recognition compared
with generic object detection. Second, a novel brands benchmark called "Open
Brands" is established. The dataset contains 1,437,812 images which have brands
and 50,000 images without any brand. The part with brands in Open Brands
contains 3,113,828 instances annotated in 3 dimensions: 4 types, 559 brands and
1216 logos. To the best of our knowledge, it is the largest dataset for brand
detection and recognition with rich annotations. We provide in-depth
comprehensive statistics about the dataset, validate the quality of the
annotations and study how the performance of many modern models evolves with an
increasing amount of training data. Third, we design a network called "Brand
Net" to handle brand recognition. Brand Net gets state-of-art mAP on Open Brand
compared with existing detection methods.
