Datasets:
pyushkevich
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crashs_template_package

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2023-08-02 23:31:16.182467: Pseudo dice [0.9144]
2023-08-02 23:31:16.182512: Epoch time: 62.45 s
2023-08-02 23:31:16.922255:
2023-08-02 23:31:16.922364: Epoch 86
2023-08-02 23:31:16.922443: Current learning rate: 0.00804
2023-08-02 23:32:19.359651: train_loss -0.9205
2023-08-02 23:32:19.359791: val_loss -0.9018
2023-08-02 23:32:19.359831: Pseudo dice [0.9188]
2023-08-02 23:32:19.359875: Epoch time: 62.44 s
2023-08-02 23:32:19.359911: Yayy! New best EMA pseudo Dice: 0.9155
2023-08-02 23:32:21.413491:
2023-08-02 23:32:21.413599: Epoch 87
2023-08-02 23:32:21.413676: Current learning rate: 0.00802
2023-08-02 23:33:23.854824: train_loss -0.9207
2023-08-02 23:33:23.854968: val_loss -0.9011
2023-08-02 23:33:23.855007: Pseudo dice [0.9188]
2023-08-02 23:33:23.855053: Epoch time: 62.44 s
2023-08-02 23:33:23.855091: Yayy! New best EMA pseudo Dice: 0.9158
2023-08-02 23:33:25.798608:
2023-08-02 23:33:25.798720: Epoch 88
2023-08-02 23:33:25.798804: Current learning rate: 0.008
2023-08-02 23:34:28.267422: train_loss -0.9182
2023-08-02 23:34:28.267566: val_loss -0.8977
2023-08-02 23:34:28.267606: Pseudo dice [0.9154]
2023-08-02 23:34:28.267652: Epoch time: 62.47 s
2023-08-02 23:34:29.022701:
2023-08-02 23:34:29.022812: Epoch 89
2023-08-02 23:34:29.022893: Current learning rate: 0.00797
2023-08-02 23:35:31.468607: train_loss -0.9203
2023-08-02 23:35:31.468749: val_loss -0.8967
2023-08-02 23:35:31.468790: Pseudo dice [0.9154]
2023-08-02 23:35:31.468837: Epoch time: 62.45 s
2023-08-02 23:35:32.209048:
2023-08-02 23:35:32.209158: Epoch 90
2023-08-02 23:35:32.209236: Current learning rate: 0.00795
2023-08-02 23:36:34.650792: train_loss -0.9231
2023-08-02 23:36:34.650940: val_loss -0.8999
2023-08-02 23:36:34.650980: Pseudo dice [0.9182]
2023-08-02 23:36:34.651027: Epoch time: 62.44 s
2023-08-02 23:36:34.651065: Yayy! New best EMA pseudo Dice: 0.916
2023-08-02 23:36:36.624382:
2023-08-02 23:36:36.624491: Epoch 91
2023-08-02 23:36:36.624573: Current learning rate: 0.00793
2023-08-02 23:37:39.096604: train_loss -0.9225
2023-08-02 23:37:39.096739: val_loss -0.9021
2023-08-02 23:37:39.096779: Pseudo dice [0.9192]
2023-08-02 23:37:39.096824: Epoch time: 62.47 s
2023-08-02 23:37:39.096862: Yayy! New best EMA pseudo Dice: 0.9163
2023-08-02 23:37:41.131934:
2023-08-02 23:37:41.132045: Epoch 92
2023-08-02 23:37:41.132128: Current learning rate: 0.0079
2023-08-02 23:38:43.583839: train_loss -0.9192
2023-08-02 23:38:43.583982: val_loss -0.8943
2023-08-02 23:38:43.584023: Pseudo dice [0.9121]
2023-08-02 23:38:43.584068: Epoch time: 62.45 s
2023-08-02 23:38:44.316833:
2023-08-02 23:38:44.316939: Epoch 93
2023-08-02 23:38:44.317021: Current learning rate: 0.00788
2023-08-02 23:39:46.780440: train_loss -0.9233
2023-08-02 23:39:46.780581: val_loss -0.9002
2023-08-02 23:39:46.780621: Pseudo dice [0.9185]
2023-08-02 23:39:46.780667: Epoch time: 62.46 s
2023-08-02 23:39:47.519527:
2023-08-02 23:39:47.519635: Epoch 94
2023-08-02 23:39:47.519714: Current learning rate: 0.00786
2023-08-02 23:40:49.953285: train_loss -0.9227
2023-08-02 23:40:49.953435: val_loss -0.898
2023-08-02 23:40:49.953475: Pseudo dice [0.916]
2023-08-02 23:40:49.953520: Epoch time: 62.43 s
2023-08-02 23:40:50.682514:
2023-08-02 23:40:50.682648: Epoch 95
2023-08-02 23:40:50.682731: Current learning rate: 0.00783
2023-08-02 23:41:53.099351: train_loss -0.9193
2023-08-02 23:41:53.099495: val_loss -0.8937
2023-08-02 23:41:53.099538: Pseudo dice [0.9119]
2023-08-02 23:41:53.099584: Epoch time: 62.42 s
2023-08-02 23:41:53.833288:
2023-08-02 23:41:53.833435: Epoch 96
2023-08-02 23:41:53.833514: Current learning rate: 0.00781
2023-08-02 23:42:56.263111: train_loss -0.9223
2023-08-02 23:42:56.263256: val_loss -0.897
2023-08-02 23:42:56.263298: Pseudo dice [0.9149]
2023-08-02 23:42:56.263343: Epoch time: 62.43 s
2023-08-02 23:42:57.002899:
2023-08-02 23:42:57.003001: Epoch 97
2023-08-02 23:42:57.003079: Current learning rate: 0.00779
2023-08-02 23:43:59.411316: train_loss -0.9204
2023-08-02 23:43:59.411462: val_loss -0.8977
2023-08-02 23:43:59.411503: Pseudo dice [0.9157]
2023-08-02 23:43:59.411548: Epoch time: 62.41 s
2023-08-02 23:44:00.275625:
2023-08-02 23:44:00.275739: Epoch 98
2023-08-02 23:44:00.275817: Current learning rate: 0.00777
2023-08-02 23:45:02.693380: train_loss -0.9195
2023-08-02 23:45:02.693525: val_loss -0.8999
2023-08-02 23:45:02.693565: Pseudo dice [0.9179]
2023-08-02 23:45:02.693609: Epoch time: 62.42 s
2023-08-02 23:45:03.435084:
2023-08-02 23:45:03.435203: Epoch 99
2023-08-02 23:45:03.435283: Current learning rate: 0.00774