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