- By Segmentation:

every algorithm is run once for every unique number of segments found in the benchmark, allowing evaluations to be performed only between segmentations with exactly the same numbers of segments - By Model:

numSegments is set separately for every model to the mode of the number of segments observed for that model in the benchmark - By Category:

numSegments is set separately for every object category to the mode of the number of segments observed for that category in the benchmark - By Shape Diameter:

numSegments is set separately for every model according to the number of segments predicted by the Shape Diameter Function (SDF) algorithm - By Core Extraction:

numSegments is set separately for every model according to the number of segments predicted by the Shape Core Extraction algorithm - By Dataset:

numSegments is set to the same number for all runs of every algorithm to the average number of segments found amongst all examples in the benchmark (which is 7)

Rand Index |
Cut Discrepancy |

Global Consistency Error |
Local Consistency Error |

Hamming Distance |
Hamming Distance - Missing Rate |

Hamming Distance - False Alarm Rate |