Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

Scalable Deep Learning Logo Detection

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically...

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

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 “LOGONet”1, a large-scale logo image database for logo detection and b...

BERKELEY LOGO 6.0 Berkeley Logo User Manual

Logo downloads

You can download our logo below with versions for printing at the bottom of the page (right click and choose "save target") Guidelines for using the logo >> [1]

Logo Nanoworlds

Logo nanoworlds are small educational environments for Logo programming that focus on the mediation of intuitive models of informatics concepts. These are coherent Gestalt-like mental concepts, which are based on experience and which are subjectively certain (Fischbein 1987). Typical features of Logo nanoworlds are Visualisation of activity defined by a Logo programme Only a few Logo syntax ele...