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test_log.txt

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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

training_metrics.log

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+epoch: 0
+{'val/Precision': tensor(0.0680, device='cuda:0'), 'val/Recall': tensor(1.0000, device='cuda:0'), 'val/IoU': tensor(0.0680, device='cuda:0'), 'val/mIoU': tensor(0.0027, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['0.00', '0.29', '0.08', '0.00', '0.00', '0.00', '0.02', '0.01', '0.00', '1.36', '0.00', '0.78', '0.00', '1.67', '0.00', '0.47', '0.49', '0.00', '0.00', '0.00']
+epoch: 0
+{'test/Precision': tensor(0.6153, device='cuda:3'), 'test/Recall': tensor(0.6188, device='cuda:3'), 'test/IoU': tensor(0.4462, device='cuda:3'), 'test/mIoU': tensor(0.1639, device='cuda:3')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.64', '32.16', '1.41', '4.93', '19.90', '15.74', '3.42', '2.48', '0.00', '59.85', '22.83', '29.91', '1.17', '24.33', '10.81', '27.35', '6.10', '35.11', '9.01', '4.86']
+epoch: 0
+{'test/Precision': tensor(0.6153, device='cuda:1'), 'test/Recall': tensor(0.6188, device='cuda:1'), 'test/IoU': tensor(0.4462, device='cuda:1'), 'test/mIoU': tensor(0.1639, device='cuda:1')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.64', '32.16', '1.41', '4.93', '19.90', '15.74', '3.42', '2.48', '0.00', '59.85', '22.83', '29.91', '1.17', '24.33', '10.81', '27.35', '6.10', '35.11', '9.01', '4.86']
+epoch: 0
+{'test/Precision': tensor(0.6153, device='cuda:2'), 'test/Recall': tensor(0.6188, device='cuda:2'), 'test/IoU': tensor(0.4462, device='cuda:2'), 'test/mIoU': tensor(0.1639, device='cuda:2')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.64', '32.16', '1.41', '4.93', '19.90', '15.74', '3.42', '2.48', '0.00', '59.85', '22.83', '29.91', '1.17', '24.33', '10.81', '27.35', '6.10', '35.11', '9.01', '4.86']
+epoch: 0
+{'test/Precision': tensor(0.6153, device='cuda:0'), 'test/Recall': tensor(0.6188, device='cuda:0'), 'test/IoU': tensor(0.4462, device='cuda:0'), 'test/mIoU': tensor(0.1639, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.64', '32.16', '1.41', '4.93', '19.90', '15.74', '3.42', '2.48', '0.00', '59.85', '22.83', '29.91', '1.17', '24.33', '10.81', '27.35', '6.10', '35.11', '9.01', '4.86']
+epoch: 0
+{'val/Precision': tensor(0.4890, device='cuda:0'), 'val/Recall': tensor(0.6651, device='cuda:0'), 'val/IoU': tensor(0.3924, device='cuda:0'), 'val/mIoU': tensor(0.1035, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['91.44', '24.85', '0.16', '0.10', '1.25', '2.21', '0.94', '0.26', '0.00', '51.27', '7.64', '25.71', '0.34', '15.82', '4.69', '21.43', '3.96', '28.74', '5.02', '2.33']
+epoch: 1
+{'val/Precision': tensor(0.5633, device='cuda:0'), 'val/Recall': tensor(0.6170, device='cuda:0'), 'val/IoU': tensor(0.4174, device='cuda:0'), 'val/mIoU': tensor(0.1152, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['92.87', '28.02', '0.22', '0.15', '4.42', '2.99', '2.33', '1.55', '0.03', '51.70', '8.90', '26.51', '0.15', '19.79', '5.32', '20.62', '5.10', '29.51', '8.61', '2.93']
+epoch: 2
+{'val/Precision': tensor(0.5763, device='cuda:0'), 'val/Recall': tensor(0.6182, device='cuda:0'), 'val/IoU': tensor(0.4250, device='cuda:0'), 'val/mIoU': tensor(0.1226, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.07', '28.95', '0.37', '0.22', '5.85', '3.03', '3.00', '2.17', '0.03', '54.40', '9.40', '27.72', '0.19', '21.22', '6.68', '24.01', '5.00', '31.44', '6.98', '2.30']
+epoch: 3
+{'val/Precision': tensor(0.5667, device='cuda:0'), 'val/Recall': tensor(0.6395, device='cuda:0'), 'val/IoU': tensor(0.4295, device='cuda:0'), 'val/mIoU': tensor(0.1303, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['92.95', '28.65', '0.38', '0.39', '8.03', '6.80', '3.51', '2.77', '0.11', '56.63', '11.24', '26.39', '0.34', '21.40', '7.60', '24.64', '3.48', '33.84', '7.82', '3.55']
+epoch: 4
+{'val/Precision': tensor(0.5687, device='cuda:0'), 'val/Recall': tensor(0.6376, device='cuda:0'), 'val/IoU': tensor(0.4298, device='cuda:0'), 'val/mIoU': tensor(0.1300, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['92.98', '29.25', '0.30', '0.37', '8.56', '4.36', '2.52', '3.42', '0.02', '55.20', '10.96', '27.68', '0.27', '21.62', '6.05', '25.62', '5.48', '32.35', '8.50', '4.41']
+epoch: 5
+{'val/Precision': tensor(0.5700, device='cuda:0'), 'val/Recall': tensor(0.6353, device='cuda:0'), 'val/IoU': tensor(0.4295, device='cuda:0'), 'val/mIoU': tensor(0.1407, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.00', '30.09', '0.42', '0.97', '13.33', '8.38', '4.15', '3.81', '0.04', '57.09', '14.06', '29.34', '0.26', '21.74', '5.13', '26.13', '7.63', '31.04', '9.15', '4.58']
+epoch: 6
+{'val/Precision': tensor(0.6036, device='cuda:0'), 'val/Recall': tensor(0.6023, device='cuda:0'), 'val/IoU': tensor(0.4316, device='cuda:0'), 'val/mIoU': tensor(0.1359, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.44', '30.00', '0.46', '0.81', '10.94', '3.30', '3.69', '3.12', '0.00', '57.36', '9.95', '29.27', '0.31', '21.43', '6.58', '25.37', '6.33', '33.26', '10.00', '6.12']
+epoch: 7
+{'val/Precision': tensor(0.6117, device='cuda:0'), 'val/Recall': tensor(0.6001, device='cuda:0'), 'val/IoU': tensor(0.4346, device='cuda:0'), 'val/mIoU': tensor(0.1341, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.54', '30.49', '0.33', '0.19', '10.34', '2.69', '2.63', '4.34', '0.00', '58.04', '13.39', '29.22', '0.17', '21.34', '6.13', '24.89', '4.90', '33.84', '8.35', '3.54']
+epoch: 8
+{'val/Precision': tensor(0.6189, device='cuda:0'), 'val/Recall': tensor(0.5982, device='cuda:0'), 'val/IoU': tensor(0.4372, device='cuda:0'), 'val/mIoU': tensor(0.1443, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.63', '31.44', '0.54', '0.41', '12.21', '8.25', '4.65', '1.61', '0.00', '57.58', '16.88', '29.62', '0.22', '22.09', '7.15', '26.07', '6.74', '34.01', '9.73', '4.90']
+epoch: 9
+{'val/Precision': tensor(0.5922, device='cuda:0'), 'val/Recall': tensor(0.6254, device='cuda:0'), 'val/IoU': tensor(0.4372, device='cuda:0'), 'val/mIoU': tensor(0.1438, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.33', '30.95', '0.47', '0.75', '11.22', '4.05', '3.24', '3.75', '0.00', '53.15', '20.05', '27.15', '0.30', '23.19', '7.96', '26.81', '8.41', '34.85', '10.72', '6.21']
+epoch: 10
+{'val/Precision': tensor(0.6134, device='cuda:0'), 'val/Recall': tensor(0.6193, device='cuda:0'), 'val/IoU': tensor(0.4454, device='cuda:0'), 'val/mIoU': tensor(0.1470, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.61', '31.21', '1.13', '1.42', '9.81', '5.42', '4.98', '5.56', '0.00', '58.51', '16.27', '28.57', '0.45', '22.32', '8.48', '26.60', '7.04', '34.86', '10.44', '6.27']
+epoch: 11
+{'val/Precision': tensor(0.5931, device='cuda:0'), 'val/Recall': tensor(0.6219, device='cuda:0'), 'val/IoU': tensor(0.4360, device='cuda:0'), 'val/mIoU': tensor(0.1508, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.33', '31.31', '0.60', '0.94', '16.05', '9.79', '2.10', '3.21', '0.01', '57.57', '19.45', '29.34', '0.32', '22.12', '8.19', '27.03', '7.32', '34.18', '10.31', '6.68']
+epoch: 12
+{'val/Precision': tensor(0.6614, device='cuda:0'), 'val/Recall': tensor(0.5494, device='cuda:0'), 'val/IoU': tensor(0.4288, device='cuda:0'), 'val/mIoU': tensor(0.1435, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.97', '30.84', '1.72', '1.25', '6.10', '8.45', '4.34', '4.10', '0.00', '58.38', '16.67', '29.42', '0.26', '23.17', '7.92', '26.33', '6.56', '31.64', '9.82', '5.75']
+epoch: 13
+{'val/Precision': tensor(0.6335, device='cuda:0'), 'val/Recall': tensor(0.5802, device='cuda:0'), 'val/IoU': tensor(0.4344, device='cuda:0'), 'val/mIoU': tensor(0.1508, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.76', '31.45', '1.14', '1.50', '12.04', '9.92', '3.67', '4.56', '0.00', '59.30', '17.93', '30.84', '0.35', '24.00', '8.73', '26.80', '6.92', '30.73', '10.44', '6.23']
+epoch: 14
+{'val/Precision': tensor(0.6135, device='cuda:0'), 'val/Recall': tensor(0.6073, device='cuda:0'), 'val/IoU': tensor(0.4393, device='cuda:0'), 'val/mIoU': tensor(0.1559, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.59', '31.51', '1.90', '1.87', '18.65', '8.77', '4.79', '4.90', '0.01', '59.55', '19.74', '29.98', '0.28', '22.16', '8.88', '26.65', '5.91', '35.57', '9.32', '5.83']
+epoch: 15
+{'val/Precision': tensor(0.5809, device='cuda:0'), 'val/Recall': tensor(0.6517, device='cuda:0'), 'val/IoU': tensor(0.4433, device='cuda:0'), 'val/mIoU': tensor(0.1560, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.20', '31.33', '0.95', '1.42', '18.21', '7.61', '4.98', '3.41', '0.00', '58.72', '19.81', '28.81', '0.56', '23.96', '9.53', '27.25', '6.75', '36.67', '10.10', '6.25']
+epoch: 16
+{'val/Precision': tensor(0.6213, device='cuda:0'), 'val/Recall': tensor(0.6018, device='cuda:0'), 'val/IoU': tensor(0.4403, device='cuda:0'), 'val/mIoU': tensor(0.1470, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.67', '30.84', '1.64', '1.87', '7.52', '10.42', '3.62', '2.04', '0.00', '58.57', '17.62', '29.85', '0.42', '23.08', '8.41', '26.20', '7.12', '34.77', '9.63', '5.77']
+epoch: 17
+{'val/Precision': tensor(0.5802, device='cuda:0'), 'val/Recall': tensor(0.6432, device='cuda:0'), 'val/IoU': tensor(0.4389, device='cuda:0'), 'val/mIoU': tensor(0.1534, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.18', '31.53', '1.18', '1.56', '14.25', '8.33', '4.32', '1.81', '0.00', '58.76', '20.60', '29.41', '0.28', '23.89', '9.62', '27.04', '7.52', '35.57', '9.96', '5.86']
+epoch: 18
+{'val/Precision': tensor(0.6088, device='cuda:0'), 'val/Recall': tensor(0.6158, device='cuda:0'), 'val/IoU': tensor(0.4412, device='cuda:0'), 'val/mIoU': tensor(0.1528, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.54', '31.02', '1.43', '2.03', '12.72', '8.91', '4.15', '3.87', '0.00', '59.21', '18.44', '29.48', '0.50', '23.83', '9.66', '27.15', '6.87', '35.06', '10.06', '6.00']
+epoch: 19
+{'val/Precision': tensor(0.6320, device='cuda:0'), 'val/Recall': tensor(0.5941, device='cuda:0'), 'val/IoU': tensor(0.4414, device='cuda:0'), 'val/mIoU': tensor(0.1525, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.79', '31.49', '1.52', '1.39', '9.60', '11.71', '3.74', '2.56', '0.00', '59.25', '18.97', '28.99', '0.31', '23.83', '9.96', '27.37', '6.93', '35.91', '10.13', '6.01']
+epoch: 20
+{'val/Precision': tensor(0.6260, device='cuda:0'), 'val/Recall': tensor(0.5956, device='cuda:0'), 'val/IoU': tensor(0.4393, device='cuda:0'), 'val/mIoU': tensor(0.1581, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.72', '31.38', '1.02', '2.76', '17.11', '13.52', '4.37', '2.78', '0.00', '59.47', '20.29', '29.79', '0.48', '23.17', '9.25', '27.02', '6.36', '35.70', '9.35', '6.57']
+epoch: 21
+{'val/Precision': tensor(0.5885, device='cuda:0'), 'val/Recall': tensor(0.6315, device='cuda:0'), 'val/IoU': tensor(0.4381, device='cuda:0'), 'val/mIoU': tensor(0.1529, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.28', '30.74', '0.92', '1.96', '17.40', '10.92', '3.71', '1.85', '0.00', '58.80', '19.84', '28.42', '0.47', '23.19', '8.92', '26.78', '6.77', '35.32', '9.35', '5.21']
+epoch: 22
+{'val/Precision': tensor(0.6390, device='cuda:0'), 'val/Recall': tensor(0.5865, device='cuda:0'), 'val/IoU': tensor(0.4405, device='cuda:0'), 'val/mIoU': tensor(0.1535, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.85', '31.51', '2.47', '3.21', '15.58', '11.54', '4.05', '1.53', '0.00', '58.94', '18.34', '29.03', '0.20', '23.91', '8.29', '26.23', '5.97', '35.93', '9.63', '5.24']
+epoch: 23
+{'val/Precision': tensor(0.6325, device='cuda:0'), 'val/Recall': tensor(0.5862, device='cuda:0'), 'val/IoU': tensor(0.4373, device='cuda:0'), 'val/mIoU': tensor(0.1531, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.77', '31.76', '0.53', '1.81', '17.12', '9.95', '3.44', '4.53', '0.00', '58.47', '21.03', '29.05', '0.43', '23.91', '8.66', '27.16', '5.86', '32.60', '9.70', '4.80']
+epoch: 24
+{'val/Precision': tensor(0.6003, device='cuda:0'), 'val/Recall': tensor(0.6301, device='cuda:0'), 'val/IoU': tensor(0.4439, device='cuda:0'), 'val/mIoU': tensor(0.1538, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.46', '31.42', '1.99', '2.57', '15.73', '11.19', '3.86', '3.48', '0.00', '55.76', '20.19', '28.65', '0.17', '24.70', '8.73', '27.43', '6.17', '36.05', '8.31', '5.89']
+epoch: 25
+{'val/Precision': tensor(0.6317, device='cuda:0'), 'val/Recall': tensor(0.6096, device='cuda:0'), 'val/IoU': tensor(0.4498, device='cuda:0'), 'val/mIoU': tensor(0.1636, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.83', '32.12', '2.28', '3.27', '18.88', '14.70', '3.70', '2.61', '0.00', '60.62', '21.88', '30.05', '0.41', '24.96', '10.53', '27.62', '6.20', '36.68', '9.11', '5.20']
+epoch: 26
+{'val/Precision': tensor(0.6294, device='cuda:0'), 'val/Recall': tensor(0.6123, device='cuda:0'), 'val/IoU': tensor(0.4500, device='cuda:0'), 'val/mIoU': tensor(0.1632, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.81', '32.18', '1.87', '3.02', '18.38', '15.30', '3.42', '2.12', '0.00', '60.81', '22.23', '30.60', '0.39', '25.20', '10.54', '27.54', '6.27', '36.34', '8.92', '4.97']
+epoch: 27
+{'val/Precision': tensor(0.6259, device='cuda:0'), 'val/Recall': tensor(0.6125, device='cuda:0'), 'val/IoU': tensor(0.4483, device='cuda:0'), 'val/mIoU': tensor(0.1635, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.76', '32.16', '1.80', '3.23', '18.86', '16.34', '3.28', '2.52', '0.00', '60.51', '22.68', '30.14', '0.49', '24.70', '10.50', '27.45', '6.28', '36.16', '8.83', '4.74']
+epoch: 28
+{'val/Precision': tensor(0.6291, device='cuda:0'), 'val/Recall': tensor(0.6104, device='cuda:0'), 'val/IoU': tensor(0.4489, device='cuda:0'), 'val/mIoU': tensor(0.1632, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.80', '32.26', '2.20', '3.47', '18.54', '15.65', '3.64', '2.34', '0.00', '60.69', '22.27', '30.18', '0.48', '24.72', '10.41', '27.51', '6.05', '36.20', '8.73', '4.73']
+epoch: 29
+{'val/Precision': tensor(0.6230, device='cuda:0'), 'val/Recall': tensor(0.6147, device='cuda:0'), 'val/IoU': tensor(0.4480, device='cuda:0'), 'val/mIoU': tensor(0.1632, device='cuda:0')}
+class_names: ('empty', 'car', 'bicycle', 'motorcycle', 'truck', 'other-vehicle', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunk', 'terrain', 'pole', 'traffic-sign')
+iou_per_class: ['93.73', '32.23', '2.30', '3.36', '19.13', '16.03', '3.31', '2.25', '0.00', '60.58', '22.36', '29.78', '0.53', '24.57', '10.84', '27.29', '6.04', '35.84', '8.83', '4.82']