Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 21:11:44.405381: val_loss -0.6919
2025-01-20 21:11:44.405436: Pseudo dice [np.float32(0.759), np.float32(0.7598), np.float32(0.8598), np.float32(0.6927), np.float32(0.9021), np.float32(0.7628)]
2025-01-20 21:11:44.405474: Epoch time: 47.76 s
2025-01-20 21:11:44.865122:
2025-01-20 21:11:44.899536: Epoch 419
2025-01-20 21:11:44.899613: Current learning rate: 0.00613
2025-01-20 21:12:32.592262: train_loss -0.7279
2025-01-20 21:12:32.627373: val_loss -0.7095
2025-01-20 21:12:32.627440: Pseudo dice [np.float32(0.7668), np.float32(0.7784), np.float32(0.8719), np.float32(0.7349), np.float32(0.8819), np.float32(0.7759)]
2025-01-20 21:12:32.627479: Epoch time: 47.73 s
2025-01-20 21:12:33.088093:
2025-01-20 21:12:33.122576: Epoch 420
2025-01-20 21:12:33.122640: Current learning rate: 0.00612
2025-01-20 21:13:20.870092: train_loss -0.7244
2025-01-20 21:13:20.905205: val_loss -0.723
2025-01-20 21:13:20.905261: Pseudo dice [np.float32(0.7707), np.float32(0.7958), np.float32(0.8701), np.float32(0.7507), np.float32(0.8857), np.float32(0.7979)]
2025-01-20 21:13:20.905303: Epoch time: 47.78 s
2025-01-20 21:13:21.364860:
2025-01-20 21:13:21.399350: Epoch 421
2025-01-20 21:13:21.399426: Current learning rate: 0.00612
2025-01-20 21:14:09.087561: train_loss -0.7221
2025-01-20 21:14:09.122675: val_loss -0.7263
2025-01-20 21:14:09.122748: Pseudo dice [np.float32(0.7754), np.float32(0.7707), np.float32(0.8637), np.float32(0.7507), np.float32(0.8758), np.float32(0.7722)]
2025-01-20 21:14:09.122793: Epoch time: 47.72 s
2025-01-20 21:14:09.581263:
2025-01-20 21:14:09.615850: Epoch 422
2025-01-20 21:14:09.615930: Current learning rate: 0.00611
2025-01-20 21:14:57.351132: train_loss -0.7249
2025-01-20 21:14:57.386250: val_loss -0.7192
2025-01-20 21:14:57.386329: Pseudo dice [np.float32(0.7663), np.float32(0.7378), np.float32(0.8635), np.float32(0.7403), np.float32(0.8966), np.float32(0.7802)]
2025-01-20 21:14:57.386388: Epoch time: 47.77 s
2025-01-20 21:14:57.850197:
2025-01-20 21:14:57.884648: Epoch 423
2025-01-20 21:14:57.884737: Current learning rate: 0.0061
2025-01-20 21:15:45.584031: train_loss -0.7337
2025-01-20 21:15:45.619140: val_loss -0.7106
2025-01-20 21:15:45.619208: Pseudo dice [np.float32(0.7595), np.float32(0.7483), np.float32(0.8614), np.float32(0.7615), np.float32(0.8883), np.float32(0.7867)]
2025-01-20 21:15:45.619244: Epoch time: 47.73 s
2025-01-20 21:15:46.078849:
2025-01-20 21:15:46.113317: Epoch 424
2025-01-20 21:15:46.113398: Current learning rate: 0.00609
2025-01-20 21:16:33.810244: train_loss -0.7274
2025-01-20 21:16:33.845352: val_loss -0.7243
2025-01-20 21:16:33.845405: Pseudo dice [np.float32(0.7625), np.float32(0.7668), np.float32(0.8718), np.float32(0.7574), np.float32(0.9064), np.float32(0.7942)]
2025-01-20 21:16:33.845442: Epoch time: 47.73 s
2025-01-20 21:16:34.305055:
2025-01-20 21:16:34.339473: Epoch 425
2025-01-20 21:16:34.339554: Current learning rate: 0.00608
2025-01-20 21:17:22.027684: train_loss -0.7195
2025-01-20 21:17:22.062712: val_loss -0.7025
2025-01-20 21:17:22.062767: Pseudo dice [np.float32(0.76), np.float32(0.7401), np.float32(0.8635), np.float32(0.7245), np.float32(0.8879), np.float32(0.7785)]
2025-01-20 21:17:22.062803: Epoch time: 47.72 s
2025-01-20 21:17:22.636276:
2025-01-20 21:17:22.670811: Epoch 426
2025-01-20 21:17:22.670888: Current learning rate: 0.00607
2025-01-20 21:18:10.428237: train_loss -0.7144
2025-01-20 21:18:10.463257: val_loss -0.7108
2025-01-20 21:18:10.463327: Pseudo dice [np.float32(0.7621), np.float32(0.7769), np.float32(0.8591), np.float32(0.7392), np.float32(0.8892), np.float32(0.7737)]
2025-01-20 21:18:10.463381: Epoch time: 47.79 s
2025-01-20 21:18:10.921554:
2025-01-20 21:18:10.956020: Epoch 427
2025-01-20 21:18:10.956096: Current learning rate: 0.00606
2025-01-20 21:18:58.706666: train_loss -0.7217
2025-01-20 21:18:58.741783: val_loss -0.7371
2025-01-20 21:18:58.741836: Pseudo dice [np.float32(0.768), np.float32(0.7701), np.float32(0.8676), np.float32(0.7601), np.float32(0.9153), np.float32(0.8008)]
2025-01-20 21:18:58.741872: Epoch time: 47.79 s
2025-01-20 21:18:59.200471:
2025-01-20 21:18:59.234941: Epoch 428
2025-01-20 21:18:59.235017: Current learning rate: 0.00605
2025-01-20 21:19:46.947984: train_loss -0.723
2025-01-20 21:19:46.983088: val_loss -0.7168
2025-01-20 21:19:46.983174: Pseudo dice [np.float32(0.7608), np.float32(0.7562), np.float32(0.862), np.float32(0.7648), np.float32(0.889), np.float32(0.7779)]
2025-01-20 21:19:46.983217: Epoch time: 47.75 s
2025-01-20 21:19:47.443774:
2025-01-20 21:19:47.478431: Epoch 429
2025-01-20 21:19:47.478510: Current learning rate: 0.00604
2025-01-20 21:20:35.191743: train_loss -0.7319
2025-01-20 21:20:35.226908: val_loss -0.7158
2025-01-20 21:20:35.226965: Pseudo dice [np.float32(0.7657), np.float32(0.7677), np.float32(0.8619), np.float32(0.7566), np.float32(0.897), np.float32(0.7886)]
2025-01-20 21:20:35.227001: Epoch time: 47.75 s
2025-01-20 21:20:35.686305:
2025-01-20 21:20:35.720831: Epoch 430
2025-01-20 21:20:35.720906: Current learning rate: 0.00603
2025-01-20 21:21:23.459231: train_loss -0.7113
2025-01-20 21:21:23.494334: val_loss -0.7182
2025-01-20 21:21:23.494405: Pseudo dice [np.float32(0.7746), np.float32(0.7759), np.float32(0.8641), np.float32(0.7595), np.float32(0.8716), np.float32(0.7567)]
2025-01-20 21:21:23.494444: Epoch time: 47.77 s
2025-01-20 21:21:23.952330:
2025-01-20 21:21:23.986802: Epoch 431
2025-01-20 21:21:23.986880: Current learning rate: 0.00602
2025-01-20 21:22:11.681782: train_loss -0.7287
2025-01-20 21:22:11.716880: val_loss -0.7038
2025-01-20 21:22:11.716934: Pseudo dice [np.float32(0.7719), np.float32(0.7771), np.float32(0.868), np.float32(0.7415), np.float32(0.8897), np.float32(0.7883)]
2025-01-20 21:22:11.716992: Epoch time: 47.73 s
2025-01-20 21:22:12.175371:
2025-01-20 21:22:12.209850: Epoch 432
2025-01-20 21:22:12.209911: Current learning rate: 0.00601
2025-01-20 21:22:59.932798: train_loss -0.7148
2025-01-20 21:22:59.967802: val_loss -0.7155
2025-01-20 21:22:59.967883: Pseudo dice [np.float32(0.7551), np.float32(0.7456), np.float32(0.8642), np.float32(0.7685), np.float32(0.8959), np.float32(0.7947)]