New: RailSem19 dataset for semantic rail scene understanding.
Results of the 2018 CVPR challenge can be seen here: semantic / instance segmentation
Welcome to the WildDash Benchmark. This website provides a dataset and benchmark for semantic and instance segmentation. We aim to improve the expressiveness of performance evaluation for computer vision algorithms in regard to their robustness for driving scenarios under real-world conditions.
We include images from a variety of data sources from all over the world with many different difficult scenarios (e.g. rain, road coverage, darkness, overexposure) and camera characteristics (noise, compression artifacts, distortion). The supplied ground truth format is compatible with Cityscapes.
diverse traffic scenarios
city, highway, and rural locations
scenes from all over the world
poor weather conditions
The main focus of this dataset is testing. It contains data recorded under real world driving situations. Aims of it are:
The WildDash dataset does not offer enough material to train algorithms by itself. We suggest you use a mixture of material from the Apollo Scape, Berkeley DeepDrive(BDD)/Nexar, Cityscapes, KITTI , and Mapillary datasets for training and the WildDash data for validation and testing.