Datasets:
pyushkevich
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crashs_template_package

Dataset card Data Studio Files
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2023-08-03 01:12:11.619192: Pseudo dice [0.922]
2023-08-03 01:12:11.619238: Epoch time: 62.49 s
2023-08-03 01:12:11.619274: Yayy! New best EMA pseudo Dice: 0.9202
2023-08-03 01:12:13.932113:
2023-08-03 01:12:13.932220: Epoch 181
2023-08-03 01:12:13.932353: Current learning rate: 0.00581
2023-08-03 01:13:16.394451: train_loss -0.9324
2023-08-03 01:13:16.394587: val_loss -0.904
2023-08-03 01:13:16.394626: Pseudo dice [0.9214]
2023-08-03 01:13:16.394670: Epoch time: 62.46 s
2023-08-03 01:13:16.394711: Yayy! New best EMA pseudo Dice: 0.9204
2023-08-03 01:13:18.510509:
2023-08-03 01:13:18.510670: Epoch 182
2023-08-03 01:13:18.510752: Current learning rate: 0.00579
2023-08-03 01:14:20.969473: train_loss -0.9337
2023-08-03 01:14:20.969612: val_loss -0.9026
2023-08-03 01:14:20.969654: Pseudo dice [0.9201]
2023-08-03 01:14:20.969701: Epoch time: 62.46 s
2023-08-03 01:14:21.731640:
2023-08-03 01:14:21.731737: Epoch 183
2023-08-03 01:14:21.731818: Current learning rate: 0.00577
2023-08-03 01:15:24.219292: train_loss -0.9329
2023-08-03 01:15:24.219433: val_loss -0.9062
2023-08-03 01:15:24.219484: Pseudo dice [0.9228]
2023-08-03 01:15:24.219532: Epoch time: 62.49 s
2023-08-03 01:15:24.219568: Yayy! New best EMA pseudo Dice: 0.9206
2023-08-03 01:15:26.455291:
2023-08-03 01:15:26.455391: Epoch 184
2023-08-03 01:15:26.455478: Current learning rate: 0.00574
2023-08-03 01:16:28.914307: train_loss -0.9323
2023-08-03 01:16:28.914437: val_loss -0.9054
2023-08-03 01:16:28.914478: Pseudo dice [0.9224]
2023-08-03 01:16:28.914522: Epoch time: 62.46 s
2023-08-03 01:16:28.914559: Yayy! New best EMA pseudo Dice: 0.9208
2023-08-03 01:16:31.087277:
2023-08-03 01:16:31.087418: Epoch 185
2023-08-03 01:16:31.087502: Current learning rate: 0.00572
2023-08-03 01:17:33.553562: train_loss -0.9326
2023-08-03 01:17:33.553698: val_loss -0.9016
2023-08-03 01:17:33.553740: Pseudo dice [0.9183]
2023-08-03 01:17:33.553786: Epoch time: 62.47 s
2023-08-03 01:17:34.309050:
2023-08-03 01:17:34.309157: Epoch 186
2023-08-03 01:17:34.309237: Current learning rate: 0.0057
2023-08-03 01:18:36.762265: train_loss -0.9331
2023-08-03 01:18:36.762405: val_loss -0.9034
2023-08-03 01:18:36.762446: Pseudo dice [0.9204]
2023-08-03 01:18:36.762491: Epoch time: 62.45 s
2023-08-03 01:18:37.517697:
2023-08-03 01:18:37.517796: Epoch 187
2023-08-03 01:18:37.517877: Current learning rate: 0.00567
2023-08-03 01:19:39.992686: train_loss -0.9334
2023-08-03 01:19:39.992829: val_loss -0.9031
2023-08-03 01:19:39.992873: Pseudo dice [0.9198]
2023-08-03 01:19:39.992918: Epoch time: 62.48 s
2023-08-03 01:19:40.749606:
2023-08-03 01:19:40.749748: Epoch 188
2023-08-03 01:19:40.749827: Current learning rate: 0.00565
2023-08-03 01:20:43.189878: train_loss -0.9351
2023-08-03 01:20:43.190023: val_loss -0.9035
2023-08-03 01:20:43.190063: Pseudo dice [0.9203]
2023-08-03 01:20:43.190107: Epoch time: 62.44 s
2023-08-03 01:20:43.949054:
2023-08-03 01:20:43.949160: Epoch 189
2023-08-03 01:20:43.949240: Current learning rate: 0.00562
2023-08-03 01:21:46.400388: train_loss -0.9346
2023-08-03 01:21:46.400533: val_loss -0.9034
2023-08-03 01:21:46.400573: Pseudo dice [0.9206]
2023-08-03 01:21:46.400616: Epoch time: 62.45 s
2023-08-03 01:21:47.275522:
2023-08-03 01:21:47.275633: Epoch 190
2023-08-03 01:21:47.275710: Current learning rate: 0.0056
2023-08-03 01:22:49.733433: train_loss -0.935
2023-08-03 01:22:49.733580: val_loss -0.9023
2023-08-03 01:22:49.733618: Pseudo dice [0.9197]
2023-08-03 01:22:49.733662: Epoch time: 62.46 s
2023-08-03 01:22:50.489857:
2023-08-03 01:22:50.489974: Epoch 191
2023-08-03 01:22:50.490054: Current learning rate: 0.00558
2023-08-03 01:23:52.932225: train_loss -0.9361
2023-08-03 01:23:52.932372: val_loss -0.9056
2023-08-03 01:23:52.932412: Pseudo dice [0.9223]
2023-08-03 01:23:52.932457: Epoch time: 62.44 s
2023-08-03 01:23:53.700439:
2023-08-03 01:23:53.700546: Epoch 192
2023-08-03 01:23:53.700626: Current learning rate: 0.00555
2023-08-03 01:24:56.171405: train_loss -0.9349
2023-08-03 01:24:56.171543: val_loss -0.9043
2023-08-03 01:24:56.171637: Pseudo dice [0.9216]
2023-08-03 01:24:56.171687: Epoch time: 62.47 s
2023-08-03 01:24:56.946766:
2023-08-03 01:24:56.946876: Epoch 193
2023-08-03 01:24:56.946957: Current learning rate: 0.00553
2023-08-03 01:25:59.399921: train_loss -0.9328
2023-08-03 01:25:59.400061: val_loss -0.9035
2023-08-03 01:25:59.400102: Pseudo dice [0.9205]
2023-08-03 01:25:59.400148: Epoch time: 62.45 s
2023-08-03 01:26:00.170328:
2023-08-03 01:26:00.170427: Epoch 194
2023-08-03 01:26:00.170509: Current learning rate: 0.0055