Segment Me If You Can

A Benchmark for Anomaly Segmentation

Meet the Team!
Robin Chan

Krzysztof Lis

Svenja Uhlemeyer

Hermann Blum

Sina Honari

Roland Siegwart

Pascal Fua

Mathieu Salzmann

Matthias Rottmann

Deep CNNs are unreliable outside of their training distribution
Perception Failures were at the Heart of Past Accidents
We benchmark semantic anomalies.
  • We benchmark the identification of semantic anomalies that do not fit into any class definitions.
  • 2 Tracks:
    • Anomaly Track: detect and localize anomaly with respect to Cityscapes.
    • Obstacle Track: detect and localize anything that is not drivable area on the road.
  • based on real-world images
Public leaderboard and submission instructions at
Anomalies can appear everywhere in the image.
Anomalies widely differ in size.
Wide variety of environments.
Labeling Policy
  • Not anomaly (white): 19 Cityscapes classes
  • Anomaly (orange): OoD objects
  • Void (black): Unknown objects in the background, gaps inside anomalies etc.
The objects of interest are the carriage including the horses. The other unknown objects in the background (e.g. parasols, chair) and gaps inside the carriage are voided.
Obstacles appear at different distances.
Different road surfaces.
Different lighting and weather conditions..
Labeling Policy
  • Not obstacle (white): Driveable area
  • Obstacle (orange): Objects placed on the road
  • Void (black): Everything besides the road, gaps inside obstacles
The road is the region of interest. The basket is labeled as obstacle, the background and the gap are ignored in the evaluation.
Anomaly Segmentation Performance Metrics
  • Classic pixel-wise metrics:
    • AUROC: Area under receiver operating characteristic curve (TPR vs. FPR)
    • AUPRC: Area under precision recall curve (precision vs. recall)
  • Recent component-wise metrics:
    • sIoU: adjusted component-wise intersection over union wrt ground truth
    • PPV: component-wise positive predictive value (or precision) wrt prediction
    • TP: sIoU greater than a given threshold τ
    • FN: sIoU smaller than a given threshold τ
    • FP: PPV smaller than a given threshold τ
    • F1(τ) := 2TP / (2TP + FP + FN) ∈ [0,1] averaged over τ=0.25,0.30,...0.75
Ordinary vs. adjusted component-wise intersection over union.
green contour: IoU=68.18% vs. sIoU=87.01%
red contour: IoU=21.68% vs. sIoU=68.44%