Datasets:
pyushkevich
/
crashs_template_package

Dataset card Data Studio Files
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1.22k
2023-08-04 06:45:56.759483: Epoch 339
2023-08-04 06:45:56.759562: Current learning rate: 0.00184
2023-08-04 06:46:55.488688: train_loss -0.9457
2023-08-04 06:46:55.488827: val_loss -0.8853
2023-08-04 06:46:55.488867: Pseudo dice [0.9067]
2023-08-04 06:46:55.488911: Epoch time: 58.73 s
2023-08-04 06:46:56.251138:
2023-08-04 06:46:56.251245: Epoch 340
2023-08-04 06:46:56.251323: Current learning rate: 0.00181
2023-08-04 06:47:54.971357: train_loss -0.9472
2023-08-04 06:47:54.971495: val_loss -0.8856
2023-08-04 06:47:54.971534: Pseudo dice [0.9069]
2023-08-04 06:47:54.971581: Epoch time: 58.72 s
2023-08-04 06:47:55.739027:
2023-08-04 06:47:55.739133: Epoch 341
2023-08-04 06:47:55.739215: Current learning rate: 0.00179
2023-08-04 06:48:54.501510: train_loss -0.9461
2023-08-04 06:48:54.501650: val_loss -0.8866
2023-08-04 06:48:54.501689: Pseudo dice [0.9073]
2023-08-04 06:48:54.501735: Epoch time: 58.76 s
2023-08-04 06:48:55.263072:
2023-08-04 06:48:55.263176: Epoch 342
2023-08-04 06:48:55.263256: Current learning rate: 0.00176
2023-08-04 06:49:54.024295: train_loss -0.9494
2023-08-04 06:49:54.024440: val_loss -0.8902
2023-08-04 06:49:54.024480: Pseudo dice [0.9098]
2023-08-04 06:49:54.024524: Epoch time: 58.76 s
2023-08-04 06:49:54.910774:
2023-08-04 06:49:54.910892: Epoch 343
2023-08-04 06:49:54.910973: Current learning rate: 0.00173
2023-08-04 06:50:53.645305: train_loss -0.9474
2023-08-04 06:50:53.645448: val_loss -0.8863
2023-08-04 06:50:53.645488: Pseudo dice [0.9073]
2023-08-04 06:50:53.645532: Epoch time: 58.74 s
2023-08-04 06:50:54.418626:
2023-08-04 06:50:54.418741: Epoch 344
2023-08-04 06:50:54.418826: Current learning rate: 0.0017
2023-08-04 06:51:53.136213: train_loss -0.9498
2023-08-04 06:51:53.136357: val_loss -0.8866
2023-08-04 06:51:53.136397: Pseudo dice [0.9079]
2023-08-04 06:51:53.136441: Epoch time: 58.72 s
2023-08-04 06:51:53.902627:
2023-08-04 06:51:53.902728: Epoch 345
2023-08-04 06:51:53.902807: Current learning rate: 0.00168
2023-08-04 06:52:52.611356: train_loss -0.9488
2023-08-04 06:52:52.611498: val_loss -0.8917
2023-08-04 06:52:52.611538: Pseudo dice [0.911]
2023-08-04 06:52:52.611583: Epoch time: 58.71 s
2023-08-04 06:52:52.611619: Yayy! New best EMA pseudo Dice: 0.9077
2023-08-04 06:52:54.565489:
2023-08-04 06:52:54.565596: Epoch 346
2023-08-04 06:52:54.565676: Current learning rate: 0.00165
2023-08-04 06:53:53.261741: train_loss -0.9468
2023-08-04 06:53:53.261893: val_loss -0.8879
2023-08-04 06:53:53.261934: Pseudo dice [0.9083]
2023-08-04 06:53:53.261979: Epoch time: 58.7 s
2023-08-04 06:53:53.262016: Yayy! New best EMA pseudo Dice: 0.9078
2023-08-04 06:53:55.282002:
2023-08-04 06:53:55.282106: Epoch 347
2023-08-04 06:53:55.282185: Current learning rate: 0.00162
2023-08-04 06:54:54.009197: train_loss -0.9505
2023-08-04 06:54:54.009343: val_loss -0.8851
2023-08-04 06:54:54.009388: Pseudo dice [0.9067]
2023-08-04 06:54:54.009435: Epoch time: 58.73 s
2023-08-04 06:54:54.895585:
2023-08-04 06:54:54.895701: Epoch 348
2023-08-04 06:54:54.895780: Current learning rate: 0.00159
2023-08-04 06:55:53.652092: train_loss -0.9476
2023-08-04 06:55:53.652392: val_loss -0.8802
2023-08-04 06:55:53.652433: Pseudo dice [0.9031]
2023-08-04 06:55:53.652477: Epoch time: 58.76 s
2023-08-04 06:55:54.417302:
2023-08-04 06:55:54.417420: Epoch 349
2023-08-04 06:55:54.417501: Current learning rate: 0.00157
2023-08-04 06:56:53.162767: train_loss -0.9481
2023-08-04 06:56:53.162904: val_loss -0.8836
2023-08-04 06:56:53.162943: Pseudo dice [0.9049]
2023-08-04 06:56:53.162989: Epoch time: 58.75 s
2023-08-04 06:56:55.109397:
2023-08-04 06:56:55.109501: Epoch 350
2023-08-04 06:56:55.109587: Current learning rate: 0.00154
2023-08-04 06:57:53.798741: train_loss -0.9472
2023-08-04 06:57:53.798887: val_loss -0.8918
2023-08-04 06:57:53.798927: Pseudo dice [0.9119]
2023-08-04 06:57:53.798972: Epoch time: 58.69 s
2023-08-04 06:57:54.561538:
2023-08-04 06:57:54.561643: Epoch 351
2023-08-04 06:57:54.561727: Current learning rate: 0.00151
2023-08-04 06:58:53.267175: train_loss -0.9471
2023-08-04 06:58:53.267319: val_loss -0.8833
2023-08-04 06:58:53.267360: Pseudo dice [0.9052]
2023-08-04 06:58:53.267404: Epoch time: 58.71 s
2023-08-04 06:58:54.032244:
2023-08-04 06:58:54.032348: Epoch 352
2023-08-04 06:58:54.032427: Current learning rate: 0.00148
2023-08-04 06:59:52.734761: train_loss -0.9471
2023-08-04 06:59:52.734901: val_loss -0.8813
2023-08-04 06:59:52.734940: Pseudo dice [0.9044]
2023-08-04 06:59:52.734983: Epoch time: 58.7 s
2023-08-04 06:59:53.619365: