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========================== Arguments ========================== |
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dataset: /tmp/codalab/tmp9okiGc/run/input/ref |
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predictions: /tmp/codalab/tmp9okiGc/run/input/res |
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datacfg: /tmp/codalab/tmp9okiGc/run/program/semantic-kitti.yaml |
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split: test |
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output: /tmp/codalab/tmp9okiGc/run/output |
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[IOU EVAL] IGNORE: [] |
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[IOU EVAL] INCLUDE: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] |
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Evaluating: 10% 20% 30% 40% 50% 60% 70% 80% 90% Done ๐. |
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========================== RESULTS ========================== |
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Validation set: |
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IoU avg 0.161 |
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IoU class 1 [car] = 0.253 |
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IoU class 2 [bicycle] = 0.020 |
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IoU class 3 [motorcycle] = 0.034 |
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IoU class 4 [truck] = 0.061 |
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IoU class 5 [other-vehicle] = 0.072 |
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IoU class 6 [person] = 0.023 |
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IoU class 7 [bicyclist] = 0.029 |
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IoU class 8 [motorcyclist] = 0.016 |
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IoU class 9 [road] = 0.589 |
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IoU class 10 [parking] = 0.302 |
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IoU class 11 [sidewalk] = 0.326 |
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IoU class 12 [other-ground] = 0.111 |
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IoU class 13 [building] = 0.274 |
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IoU class 14 [fence] = 0.188 |
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IoU class 15 [vegetation] = 0.269 |
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IoU class 16 [trunk] = 0.093 |
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IoU class 17 [terrain] = 0.270 |
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IoU class 18 [pole] = 0.068 |
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IoU class 19 [traffic-sign] = 0.069 |
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Precision = 63.26 |
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Recall = 60.15 |
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IoU Cmpltn = 44.58 |
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mIoU SSC = 16.15 |
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