The Cityscapes Dataset
We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available at this webpage.
The Cityscapes Dataset is intended for
- assessing the performance of vision algorithms for two major tasks of semantic urban scene understanding: pixel-level and instance-level semantic labeling;
- supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks.
This Cityscapes Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.