Datasets:
pyushkevich
/
crashs_template_package

Dataset card Data Studio Files
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2023-08-03 00:13:49.510624:
2023-08-03 00:13:49.510729: Epoch 126
2023-08-03 00:13:49.510810: Current learning rate: 0.00711
2023-08-03 00:14:51.978587: train_loss -0.9257
2023-08-03 00:14:51.978733: val_loss -0.9035
2023-08-03 00:14:51.978774: Pseudo dice [0.9208]
2023-08-03 00:14:51.978820: Epoch time: 62.47 s
2023-08-03 00:14:51.978858: Yayy! New best EMA pseudo Dice: 0.9183
2023-08-03 00:14:54.091730:
2023-08-03 00:14:54.091836: Epoch 127
2023-08-03 00:14:54.091920: Current learning rate: 0.00709
2023-08-03 00:15:56.543871: train_loss -0.9271
2023-08-03 00:15:56.544451: val_loss -0.9034
2023-08-03 00:15:56.544496: Pseudo dice [0.9208]
2023-08-03 00:15:56.544542: Epoch time: 62.45 s
2023-08-03 00:15:56.544580: Yayy! New best EMA pseudo Dice: 0.9186
2023-08-03 00:15:58.737329:
2023-08-03 00:15:58.737431: Epoch 128
2023-08-03 00:15:58.737510: Current learning rate: 0.00707
2023-08-03 00:17:01.174269: train_loss -0.9291
2023-08-03 00:17:01.174399: val_loss -0.9013
2023-08-03 00:17:01.174441: Pseudo dice [0.9194]
2023-08-03 00:17:01.174486: Epoch time: 62.44 s
2023-08-03 00:17:01.174522: Yayy! New best EMA pseudo Dice: 0.9187
2023-08-03 00:17:03.353902:
2023-08-03 00:17:03.354011: Epoch 129
2023-08-03 00:17:03.354094: Current learning rate: 0.00704
2023-08-03 00:18:05.821488: train_loss -0.928
2023-08-03 00:18:05.821628: val_loss -0.9004
2023-08-03 00:18:05.821673: Pseudo dice [0.9182]
2023-08-03 00:18:05.821724: Epoch time: 62.47 s
2023-08-03 00:18:06.590420:
2023-08-03 00:18:06.590520: Epoch 130
2023-08-03 00:18:06.590607: Current learning rate: 0.00702
2023-08-03 00:19:09.051989: train_loss -0.9293
2023-08-03 00:19:09.052130: val_loss -0.901
2023-08-03 00:19:09.052169: Pseudo dice [0.918]
2023-08-03 00:19:09.052217: Epoch time: 62.46 s
2023-08-03 00:19:09.811503:
2023-08-03 00:19:09.811609: Epoch 131
2023-08-03 00:19:09.811686: Current learning rate: 0.007
2023-08-03 00:20:12.236447: train_loss -0.926
2023-08-03 00:20:12.236587: val_loss -0.9031
2023-08-03 00:20:12.236628: Pseudo dice [0.9203]
2023-08-03 00:20:12.236675: Epoch time: 62.43 s
2023-08-03 00:20:12.236713: Yayy! New best EMA pseudo Dice: 0.9187
2023-08-03 00:20:14.293143:
2023-08-03 00:20:14.293251: Epoch 132
2023-08-03 00:20:14.293334: Current learning rate: 0.00697
2023-08-03 00:21:16.720473: train_loss -0.927
2023-08-03 00:21:16.720614: val_loss -0.9021
2023-08-03 00:21:16.720654: Pseudo dice [0.9194]
2023-08-03 00:21:16.720699: Epoch time: 62.43 s
2023-08-03 00:21:16.720736: Yayy! New best EMA pseudo Dice: 0.9188
2023-08-03 00:21:18.793363:
2023-08-03 00:21:18.793465: Epoch 133
2023-08-03 00:21:18.793542: Current learning rate: 0.00695
2023-08-03 00:22:21.212766: train_loss -0.926
2023-08-03 00:22:21.212905: val_loss -0.9035
2023-08-03 00:22:21.212946: Pseudo dice [0.9201]
2023-08-03 00:22:21.212990: Epoch time: 62.42 s
2023-08-03 00:22:21.213027: Yayy! New best EMA pseudo Dice: 0.9189
2023-08-03 00:22:23.378061:
2023-08-03 00:22:23.378209: Epoch 134
2023-08-03 00:22:23.378296: Current learning rate: 0.00693
2023-08-03 00:23:25.850702: train_loss -0.9281
2023-08-03 00:23:25.850832: val_loss -0.8993
2023-08-03 00:23:25.850873: Pseudo dice [0.9174]
2023-08-03 00:23:25.850919: Epoch time: 62.47 s
2023-08-03 00:23:26.615247:
2023-08-03 00:23:26.615359: Epoch 135
2023-08-03 00:23:26.615434: Current learning rate: 0.0069
2023-08-03 00:24:29.101566: train_loss -0.9279
2023-08-03 00:24:29.101701: val_loss -0.9008
2023-08-03 00:24:29.101741: Pseudo dice [0.9176]
2023-08-03 00:24:29.101787: Epoch time: 62.49 s
2023-08-03 00:24:29.876724:
2023-08-03 00:24:29.876830: Epoch 136
2023-08-03 00:24:29.876908: Current learning rate: 0.00688
2023-08-03 00:25:32.342974: train_loss -0.9249
2023-08-03 00:25:32.343115: val_loss -0.9006
2023-08-03 00:25:32.343154: Pseudo dice [0.9182]
2023-08-03 00:25:32.343198: Epoch time: 62.47 s
2023-08-03 00:25:33.108534:
2023-08-03 00:25:33.108640: Epoch 137
2023-08-03 00:25:33.108721: Current learning rate: 0.00686
2023-08-03 00:26:35.554161: train_loss -0.9282
2023-08-03 00:26:35.554305: val_loss -0.8982
2023-08-03 00:26:35.554349: Pseudo dice [0.9168]
2023-08-03 00:26:35.554399: Epoch time: 62.45 s
2023-08-03 00:26:36.318381:
2023-08-03 00:26:36.318484: Epoch 138
2023-08-03 00:26:36.318564: Current learning rate: 0.00683
2023-08-03 00:27:38.744359: train_loss -0.9267
2023-08-03 00:27:38.744502: val_loss -0.9011
2023-08-03 00:27:38.744543: Pseudo dice [0.9181]
2023-08-03 00:27:38.744588: Epoch time: 62.43 s
2023-08-03 00:27:39.508059:
2023-08-03 00:27:39.508159: Epoch 139
2023-08-03 00:27:39.508238: Current learning rate: 0.00681