  Logo detection from images has many applications, particularly for brand
recognition and intellectual property protection. Most existing studies for
logo recognition and detection are based on small-scale datasets which are not
comprehensive enough when exploring emerging deep learning techniques. In this
paper, we introduce "LOGO-Net", a large-scale logo image database for logo
detection and brand recognition from real-world product images. To facilitate
research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes,
10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo
classes, 100 brands, and 130,608 logo objects. We describe the ideas and
challenges for constructing such a large-scale database. Another key
contribution of this work is to apply emerging deep learning techniques for
logo detection and brand recognition tasks, and conduct extensive experiments
by exploring several state-of-the-art deep region-based convolutional networks
techniques for object detection tasks. The LOGO-net will be released at
http://logo-net.org/
