Datasets:
pyushkevich
/
crashs_template_package

Dataset card Data Studio Files
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1.22k
2023-08-04 06:59:53.619483: Epoch 353
2023-08-04 06:59:53.619558: Current learning rate: 0.00146
2023-08-04 07:00:52.316304: train_loss -0.9488
2023-08-04 07:00:52.316449: val_loss -0.8862
2023-08-04 07:00:52.316489: Pseudo dice [0.9074]
2023-08-04 07:00:52.316534: Epoch time: 58.7 s
2023-08-04 07:00:53.078764:
2023-08-04 07:00:53.078880: Epoch 354
2023-08-04 07:00:53.078960: Current learning rate: 0.00143
2023-08-04 07:01:51.763517: train_loss -0.9475
2023-08-04 07:01:51.763691: val_loss -0.8856
2023-08-04 07:01:51.763731: Pseudo dice [0.9074]
2023-08-04 07:01:51.763775: Epoch time: 58.69 s
2023-08-04 07:01:52.526966:
2023-08-04 07:01:52.527079: Epoch 355
2023-08-04 07:01:52.527158: Current learning rate: 0.0014
2023-08-04 07:02:51.276863: train_loss -0.9465
2023-08-04 07:02:51.277000: val_loss -0.8862
2023-08-04 07:02:51.277044: Pseudo dice [0.9062]
2023-08-04 07:02:51.277091: Epoch time: 58.75 s
2023-08-04 07:02:52.049050:
2023-08-04 07:02:52.049161: Epoch 356
2023-08-04 07:02:52.049242: Current learning rate: 0.00137
2023-08-04 07:03:50.753988: train_loss -0.9471
2023-08-04 07:03:50.754133: val_loss -0.8875
2023-08-04 07:03:50.754176: Pseudo dice [0.9086]
2023-08-04 07:03:50.754219: Epoch time: 58.71 s
2023-08-04 07:03:51.529985:
2023-08-04 07:03:51.530093: Epoch 357
2023-08-04 07:03:51.530172: Current learning rate: 0.00134
2023-08-04 07:04:50.249002: train_loss -0.9495
2023-08-04 07:04:50.249146: val_loss -0.8809
2023-08-04 07:04:50.249189: Pseudo dice [0.9039]
2023-08-04 07:04:50.249238: Epoch time: 58.72 s
2023-08-04 07:04:51.044468:
2023-08-04 07:04:51.044572: Epoch 358
2023-08-04 07:04:51.044650: Current learning rate: 0.00132
2023-08-04 07:05:49.722384: train_loss -0.9504
2023-08-04 07:05:49.722524: val_loss -0.8904
2023-08-04 07:05:49.722564: Pseudo dice [0.9095]
2023-08-04 07:05:49.722609: Epoch time: 58.68 s
2023-08-04 07:05:50.624743:
2023-08-04 07:05:50.624867: Epoch 359
2023-08-04 07:05:50.624948: Current learning rate: 0.00129
2023-08-04 07:06:49.328923: train_loss -0.9493
2023-08-04 07:06:49.329065: val_loss -0.8865
2023-08-04 07:06:49.329107: Pseudo dice [0.9075]
2023-08-04 07:06:49.329151: Epoch time: 58.7 s
2023-08-04 07:06:50.088752:
2023-08-04 07:06:50.088877: Epoch 360
2023-08-04 07:06:50.088955: Current learning rate: 0.00126
2023-08-04 07:07:48.804535: train_loss -0.9501
2023-08-04 07:07:48.804670: val_loss -0.8882
2023-08-04 07:07:48.804709: Pseudo dice [0.909]
2023-08-04 07:07:48.804753: Epoch time: 58.72 s
2023-08-04 07:07:49.565823:
2023-08-04 07:07:49.565930: Epoch 361
2023-08-04 07:07:49.566011: Current learning rate: 0.00123
2023-08-04 07:08:48.267787: train_loss -0.949
2023-08-04 07:08:48.267926: val_loss -0.8908
2023-08-04 07:08:48.267966: Pseudo dice [0.9109]
2023-08-04 07:08:48.268010: Epoch time: 58.7 s
2023-08-04 07:08:49.033210:
2023-08-04 07:08:49.033317: Epoch 362
2023-08-04 07:08:49.033395: Current learning rate: 0.0012
2023-08-04 07:09:47.763478: train_loss -0.9489
2023-08-04 07:09:47.763620: val_loss -0.8882
2023-08-04 07:09:47.763659: Pseudo dice [0.9097]
2023-08-04 07:09:47.763703: Epoch time: 58.73 s
2023-08-04 07:09:47.763739: Yayy! New best EMA pseudo Dice: 0.9079
2023-08-04 07:09:49.711214:
2023-08-04 07:09:49.711318: Epoch 363
2023-08-04 07:09:49.711398: Current learning rate: 0.00117
2023-08-04 07:10:48.418472: train_loss -0.9507
2023-08-04 07:10:48.418618: val_loss -0.8845
2023-08-04 07:10:48.418658: Pseudo dice [0.9054]
2023-08-04 07:10:48.418703: Epoch time: 58.71 s
2023-08-04 07:10:49.308873:
2023-08-04 07:10:49.308985: Epoch 364
2023-08-04 07:10:49.309065: Current learning rate: 0.00115
2023-08-04 07:11:48.029142: train_loss -0.95
2023-08-04 07:11:48.029289: val_loss -0.8895
2023-08-04 07:11:48.029328: Pseudo dice [0.9095]
2023-08-04 07:11:48.029373: Epoch time: 58.72 s
2023-08-04 07:11:48.803672:
2023-08-04 07:11:48.803777: Epoch 365
2023-08-04 07:11:48.803858: Current learning rate: 0.00112
2023-08-04 07:12:47.519648: train_loss -0.949
2023-08-04 07:12:47.519787: val_loss -0.883
2023-08-04 07:12:47.519828: Pseudo dice [0.9057]
2023-08-04 07:12:47.519873: Epoch time: 58.72 s
2023-08-04 07:12:48.292556:
2023-08-04 07:12:48.292665: Epoch 366
2023-08-04 07:12:48.292744: Current learning rate: 0.00109
2023-08-04 07:13:46.996671: train_loss -0.9501
2023-08-04 07:13:46.996814: val_loss -0.877
2023-08-04 07:13:46.996855: Pseudo dice [0.9011]
2023-08-04 07:13:46.996901: Epoch time: 58.7 s
2023-08-04 07:13:47.770773:
2023-08-04 07:13:47.770884: Epoch 367