Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 21:22:59.967926: Epoch time: 47.76 s
2025-01-20 21:23:00.424870:
2025-01-20 21:23:00.459364: Epoch 433
2025-01-20 21:23:00.459450: Current learning rate: 0.006
2025-01-20 21:23:48.143830: train_loss -0.725
2025-01-20 21:23:48.178908: val_loss -0.7056
2025-01-20 21:23:48.178964: Pseudo dice [np.float32(0.7751), np.float32(0.8098), np.float32(0.8699), np.float32(0.7627), np.float32(0.9032), np.float32(0.7992)]
2025-01-20 21:23:48.179002: Epoch time: 47.72 s
2025-01-20 21:23:48.637325:
2025-01-20 21:23:48.671787: Epoch 434
2025-01-20 21:23:48.671875: Current learning rate: 0.00599
2025-01-20 21:24:36.348892: train_loss -0.7195
2025-01-20 21:24:36.383863: val_loss -0.7222
2025-01-20 21:24:36.383961: Pseudo dice [np.float32(0.7717), np.float32(0.7819), np.float32(0.8524), np.float32(0.7644), np.float32(0.8921), np.float32(0.7882)]
2025-01-20 21:24:36.383998: Epoch time: 47.71 s
2025-01-20 21:24:36.957514:
2025-01-20 21:24:36.992043: Epoch 435
2025-01-20 21:24:36.992117: Current learning rate: 0.00598
2025-01-20 21:25:24.704280: train_loss -0.7184
2025-01-20 21:25:24.739423: val_loss -0.7325
2025-01-20 21:25:24.739520: Pseudo dice [np.float32(0.7689), np.float32(0.7786), np.float32(0.8701), np.float32(0.7833), np.float32(0.8944), np.float32(0.7883)]
2025-01-20 21:25:24.739558: Epoch time: 47.75 s
2025-01-20 21:25:25.200332:
2025-01-20 21:25:25.234793: Epoch 436
2025-01-20 21:25:25.234859: Current learning rate: 0.00597
2025-01-20 21:26:12.989819: train_loss -0.7292
2025-01-20 21:26:13.024998: val_loss -0.7166
2025-01-20 21:26:13.025053: Pseudo dice [np.float32(0.7699), np.float32(0.746), np.float32(0.864), np.float32(0.7703), np.float32(0.8981), np.float32(0.7886)]
2025-01-20 21:26:13.025090: Epoch time: 47.79 s
2025-01-20 21:26:13.485108:
2025-01-20 21:26:13.519598: Epoch 437
2025-01-20 21:26:13.519662: Current learning rate: 0.00596
2025-01-20 21:27:01.253083: train_loss -0.7292
2025-01-20 21:27:01.288069: val_loss -0.7084
2025-01-20 21:27:01.288126: Pseudo dice [np.float32(0.7618), np.float32(0.7899), np.float32(0.8452), np.float32(0.746), np.float32(0.8927), np.float32(0.7798)]
2025-01-20 21:27:01.288162: Epoch time: 47.77 s
2025-01-20 21:27:01.747624:
2025-01-20 21:27:01.782108: Epoch 438
2025-01-20 21:27:01.782170: Current learning rate: 0.00595
2025-01-20 21:27:49.516561: train_loss -0.7214
2025-01-20 21:27:49.551548: val_loss -0.7021
2025-01-20 21:27:49.551603: Pseudo dice [np.float32(0.7657), np.float32(0.7823), np.float32(0.8611), np.float32(0.7591), np.float32(0.8964), np.float32(0.7904)]
2025-01-20 21:27:49.551640: Epoch time: 47.77 s
2025-01-20 21:27:50.013514:
2025-01-20 21:27:50.048005: Epoch 439
2025-01-20 21:27:50.048069: Current learning rate: 0.00594
2025-01-20 21:28:37.752010: train_loss -0.7327
2025-01-20 21:28:37.787124: val_loss -0.719
2025-01-20 21:28:37.787179: Pseudo dice [np.float32(0.7708), np.float32(0.7853), np.float32(0.8772), np.float32(0.7653), np.float32(0.8952), np.float32(0.7703)]
2025-01-20 21:28:37.787220: Epoch time: 47.74 s
2025-01-20 21:28:38.245995:
2025-01-20 21:28:38.280546: Epoch 440
2025-01-20 21:28:38.280608: Current learning rate: 0.00593
2025-01-20 21:29:26.036340: train_loss -0.7342
2025-01-20 21:29:26.071454: val_loss -0.7139
2025-01-20 21:29:26.071508: Pseudo dice [np.float32(0.7642), np.float32(0.7729), np.float32(0.8688), np.float32(0.7532), np.float32(0.8925), np.float32(0.774)]
2025-01-20 21:29:26.071546: Epoch time: 47.79 s
2025-01-20 21:29:26.532774:
2025-01-20 21:29:26.567298: Epoch 441
2025-01-20 21:29:26.567361: Current learning rate: 0.00592
2025-01-20 21:30:14.277517: train_loss -0.7307
2025-01-20 21:30:14.312638: val_loss -0.7233
2025-01-20 21:30:14.312692: Pseudo dice [np.float32(0.7816), np.float32(0.7748), np.float32(0.8718), np.float32(0.7895), np.float32(0.8951), np.float32(0.7871)]
2025-01-20 21:30:14.312737: Epoch time: 47.75 s
2025-01-20 21:30:14.770633:
2025-01-20 21:30:14.805087: Epoch 442
2025-01-20 21:30:14.805150: Current learning rate: 0.00592
2025-01-20 21:31:02.484788: train_loss -0.7204
2025-01-20 21:31:02.519902: val_loss -0.7159
2025-01-20 21:31:02.519958: Pseudo dice [np.float32(0.7719), np.float32(0.7685), np.float32(0.8651), np.float32(0.765), np.float32(0.8846), np.float32(0.7622)]
2025-01-20 21:31:02.519997: Epoch time: 47.71 s
2025-01-20 21:31:02.985468:
2025-01-20 21:31:03.019949: Epoch 443
2025-01-20 21:31:03.020019: Current learning rate: 0.00591
2025-01-20 21:31:50.736505: train_loss -0.7273
2025-01-20 21:31:50.772246: val_loss -0.7232
2025-01-20 21:31:50.772331: Pseudo dice [np.float32(0.7671), np.float32(0.751), np.float32(0.8675), np.float32(0.7603), np.float32(0.9086), np.float32(0.7929)]
2025-01-20 21:31:50.772369: Epoch time: 47.75 s
2025-01-20 21:31:51.335438:
2025-01-20 21:31:51.369962: Epoch 444
2025-01-20 21:31:51.370067: Current learning rate: 0.0059
2025-01-20 21:32:39.082218: train_loss -0.7245
2025-01-20 21:32:39.117350: val_loss -0.7074
2025-01-20 21:32:39.117422: Pseudo dice [np.float32(0.7665), np.float32(0.7671), np.float32(0.8652), np.float32(0.7822), np.float32(0.8891), np.float32(0.8068)]
2025-01-20 21:32:39.117476: Epoch time: 47.75 s
2025-01-20 21:32:39.571072:
2025-01-20 21:32:39.605532: Epoch 445
2025-01-20 21:32:39.605606: Current learning rate: 0.00589
2025-01-20 21:33:27.375771: train_loss -0.7245
2025-01-20 21:33:27.410872: val_loss -0.7221
2025-01-20 21:33:27.410928: Pseudo dice [np.float32(0.7773), np.float32(0.784), np.float32(0.863), np.float32(0.7706), np.float32(0.9011), np.float32(0.8016)]
2025-01-20 21:33:27.410966: Epoch time: 47.81 s
2025-01-20 21:33:27.864532:
2025-01-20 21:33:27.899002: Epoch 446
2025-01-20 21:33:27.899063: Current learning rate: 0.00588
2025-01-20 21:34:15.658483: train_loss -0.7254
2025-01-20 21:34:15.693597: val_loss -0.7172
2025-01-20 21:34:15.693652: Pseudo dice [np.float32(0.7617), np.float32(0.7606), np.float32(0.8494), np.float32(0.7512), np.float32(0.8901), np.float32(0.8056)]
2025-01-20 21:34:15.693688: Epoch time: 47.79 s
2025-01-20 21:34:16.150431: