QMUL OpenLogo


Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. Further, we create an open logo detection benchmark, called QMUL-OpenLogo, to promote the investigation of this new challenge. QMUL-OpenLogo contains 27,083 images from 352 logo classes, built by aggregating and refining 7 existing datasets and establishing an open logo detection evaluation protocol.


For dataset training-evaluation split, we propose 2 kind of setting. The first one is the fully supervised setting, with every logo classes contains 70% of training split and 30% of evaluation split. The second setting split the dataset into 32 supervised classes and 320 unsupervised classes, where the supervised contains real training split and evaluation split, while the unsupervised classes have no training split, only evaluation split.

Notification for Supervised/Unsupervised splitting: The dataset contains images of multi-instances and multi-classes, thus some supervised set images may contain both supervised and unsupervised instances in it, it is recommendded to filter it for own useage.

Fully Supervised Split

Classes Train Images Val Images Test Images Total Images
352 15,975 2,777 8,331 27,083

Semi-Supervised Split

Type Classes Train Images Val Images Test Images Total Images
Supervised 32 1,280 1,019 9,168 11,467
Unsupervised 320 0 1,562 14,054 15,616
Total 352 1,280 2,581 23,222 27,083


  • Nov 7, 2018:
  • July 03, 2018: Dataset released.


QMUL-OpenLogo Dataset (4.7 GB): [Google Drive]

Supplyment Imageset List for OpenLogo Semi-Supervised Challenge: [Google Drive]


	    	Open Logo Detection Challenge.
		Hang Su, Xiatian Zhu and Shaogang Gong.
		In Proc. British Machine Vision Conference (BMVC), Newcastle, UK, September 2018.
		BibTex arxiv


Please notice that, the QMUL-OpenLogo Dataset is made available for academic research purpose only. All the images were collected from the existing logo detection datasets, and the copyright belongs to the original owners.


Please feel free to send any questions and/or comments to Hang Su at hang.su@qmul.ac.uk