WebLogo-2M Dataset


Hang Su     Shaogang Gong     Xiatian Zhu

Computer Vision Group, School of Electronic Engineering and Computer Science, Queen Mary University of London

Description

The WebLogo-2M dataset is a weakly labelled (at image level rather than object bounding box level) logo detection dataset. The dataset was constructed automatically by sampling the Twitter stream data. It contains 194 unique logo classes and over 2 million logo images. It features with large scale but very noisy labels across logos due to the inherent nature of web data. Generally, these weakly labelled logo images are used for model training. Therefore, this dataset is designed for large-scale logo detection model learning from noisy training data with high computational challenges. For performance evaluation, we further provide 6, 569 test images with manually labelled logo bounding boxes for all the 194 logo classes.

Statistics

Logo Classes Image Pool Per-Class Noise Rate Testing Images
194 2,190,757 25.0%~99.8% 6,569

Icons of all 194 logo classes

Download

Total size: 178.1GB

Part 1 (3m-android, 24.9GB); Part 2 (apple-citi, 21.2GB); Part 3 (coach-evernote, 21.4GB); Part 4 (facebook-homedepot, 25.1GB); Part 5 (honda-mobil, 20.4GB); Part 6 (motorola-porsche, 21.9GB); Part 7 (prada-wii, 23.1GB); Part 8 (windows-zara, 20.3GB); Evaluation/Test Data (1.1GB); Logo Icons; README

  • 7/March/2018: Added logo icons download link.
  • 25/Aug/2017: upgraded from 1.9M (1,867,177) to 2.2M (2,190,757) total logo images

Reference

Hang Su, Xiatian Zhu, Shaogang Gong.
Deep Learning Logo Detection with Data Expansion by Synthesising Context
IEEE Winter Conference on Applications of Computer Science (WACV), Santa Rosa, USA, March 2017. 
arxiv  bibtex
Hang Su, Shaogang Gong, Xiatian Zhu.
WebLogo-2M: Scalable Logo Detection by Deep Learning from the Web
IEEE International Conference on Computer Vision, Workshop on Web-scale Vision and Social Media, 
Venice, Italy, October 2017.
arxivbibtex

Contact

For any queries, please contact Hang Su at hang.su@qmul.ac.uk

Licence

Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any images belong to you and you would like them to be removed, please kindly inform us.