spvnas_10.8M.txt

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INTERFACE:
Data:  /tmp/codalab/tmp9QIqYC/run/input/ref
Predictions:  /tmp/codalab/tmp9QIqYC/run/input/res
Backend:  numpy
Split:  test
Config:  /tmp/codalab/tmp9QIqYC/run/program/semantic-kitti.yaml
Limit:  None
Codalab:  /tmp/codalab/tmp9QIqYC/run/output
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Opening data config file /tmp/codalab/tmp9QIqYC/run/program/semantic-kitti.yaml
Ignoring xentropy class  0  in IoU evaluation
[IOU EVAL] IGNORE:  [0]
[IOU EVAL] INCLUDE:  [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]
Evaluating sequences: 10% 20% 30% 40% 50% 60% 70% 80% 90% Validation set:
Acc avg 0.907
IoU avg 0.623
IoU class 1 [car] = 0.967
IoU class 2 [bicycle] = 0.406
IoU class 3 [motorcycle] = 0.421
IoU class 4 [truck] = 0.509
IoU class 5 [other-vehicle] = 0.513
IoU class 6 [person] = 0.604
IoU class 7 [bicyclist] = 0.628
IoU class 8 [motorcyclist] = 0.218
IoU class 9 [road] = 0.896
IoU class 10 [parking] = 0.632
IoU class 11 [sidewalk] = 0.738
IoU class 12 [other-ground] = 0.291
IoU class 13 [building] = 0.909
IoU class 14 [fence] = 0.653
IoU class 15 [vegetation] = 0.855
IoU class 16 [trunk] = 0.703
IoU class 17 [terrain] = 0.698
IoU class 18 [pole] = 0.576
IoU class 19 [traffic-sign] = 0.620
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below can be copied straight for paper table
0.967,0.406,0.421,0.509,0.513,0.604,0.628,0.218,0.896,0.632,0.738,0.291,0.909,0.653,0.855,0.703,0.698,0.576,0.620,0.623,0.907