A Unified Dataset to Expand the Scope of Semantic Segmentation

Ana Martin Doncel and Marcos Escudero-Viñolo

Video Processing and Understanding Lab (VPU Lab), Universidad Autónoma de Madrid

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Our aim is to create a unified semantic dataset which—by enlarging the number of classesand the diversity of the shared classes, aims to provide a more generic benchmark for training and evaluation of semantic segmentation methods.

The created dataset contains images obtained from different datasets re-labeled with a bigger variety of labels, obtaining a total of 293 semantic labels distributed among 145,555 training images and 7,614 validation images (each of one with its own annotation).

The datasets agglutinated to compose the proposed dataset are:


Documentation is under development

Acknowledgement: This study has been partially supported by the Spanish Government through its TEC2017-88169-R MobiNetVideo project.