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
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.
No associated paper or article has been found for this dataset.
Annotations consist of simple image labels denoting the logo of each image.
You can download the dataset here.
Four models have been provided for this dataset: YOLOv2 + SCL, FR-CNN + SCL, YOLOv2 + CAL and FR-CNN + CAL.
The official benchmark can be consulted here.
No associated challenges have been found for this dataset.
Dataset licenced under a Non-Commercial licence.