WildDash Benchmark

We are excited to be part of the Robust Vision Challenge 2018. Check out the challenge website and our submission instructions for further details on how to participate. We are looking forward to seeing you at CVPR!

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 under real-world conditions.

Diverse and Challenging Scene Content

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

Focus on Robustness and Performance Evaluation

The main focus of this dataset is testing. It does not offer enough material to train algorithms by itself. We suggest you use a mixture of material from the Cityscapes, Berkeley DeepDrive(BDD)/Nexar, and Mapillary datasets for training and the WildDash data for validation and testing.