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 major tasks of semantic urban scene understanding: pixel-level, instance-level, and panoptic semantic labeling;
- supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks.
- Coming Soon: Cityscapes 3DCityscapes 3D is an extension of the original Cityscapes with 3D bounding box annotations for all types of vehicles as well as a benchmark for the 3D detection task. For more details please refer to our paper, presented at the CVPR 2020 Workshop on Scalability in Autonomous Driving. Both, dataset and benchmark will be released soon. Stay tuned!
- Robust Vision Challenge 2020The Cityscapes dataset is again part of the Robust Vision Challenge. The goal of this challenge is to foster the development of vision systems that are robust and consequently perform well on a variety of datasets with different characteristics. Towards this goal, performance is measured across a number of challenging benchmarks with different characteristics, e.g., indoors vs. outdoors, real vs. synthetic, sunny vs. bad weather, different sensors. Methods that participate in the semantic, instance, or panoptic task need to submit their results to our evaluation server.
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.