Segment Me If You Can

A Benchmark for Anomaly Segmentation
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Real World Datasets

Get some details about our datasets RoadAnomaly21 & RoadObstacle21 and our labeling policy. You can download the images here.

Public Leaderboard

Get an overview about already evaluated methods, listed in the public leaderboard. Beat the highscore if you can!

Benchmark Suite

Analyze your method in our benchmark suite. We provide established pixel-wise as well as recent component-wise performance metrics.

SegmentMeIfYouCan - A Benchmark for Anomaly Segmentation

The detection and localization of previously-unseen objects is of utmost importance for safety-critical applications such as perception for automated driving, especially if such unknown objects appear on the road ahead. Our benchmark addresses two tasks: Anomalous object segmentation, which considers any previously-unseen object category; and road obstacle segmentation, which focuses on any object on the road, may it be known or unknown. We provide two corresponding datasets together with a test suite, performing an in-depth method analysis.

Figure: Qualitative comparison of anomaly scores produced by different anomaly segmentation methods for one example from RoadAnomaly21.