Annotations are freely available for research purposes. Please download every part and then unrar them
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:
COCO-Stuff, COCO-Stuff Dataset:Tran/Val 2017 Images and Annotations
Cityscapes, Cityscapes Dataset
ADE20K, ADE20K Dataset
TASKONOMY, TASKONOMY Dataset
Mapillary, Mapillary Dataset
Documentation is under development
Acknowledgement: This study has been partially supported by the Spanish Government through its TEC2017-88169-R MobiNetVideo project.