Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 16:32:44.263316: Epoch time: 47.82 s
2025-01-20 16:32:44.263337: Yayy! New best EMA pseudo Dice: 0.7764000296592712
2025-01-20 16:32:45.219532:
2025-01-20 16:32:45.223049: Epoch 73
2025-01-20 16:32:45.223165: Current learning rate: 0.00934
2025-01-20 16:33:33.032661: train_loss -0.6859
2025-01-20 16:33:33.067817: val_loss -0.6979
2025-01-20 16:33:33.067895: Pseudo dice [np.float32(0.7355), np.float32(0.7648), np.float32(0.8474), np.float32(0.7365), np.float32(0.8898), np.float32(0.765)]
2025-01-20 16:33:33.067932: Epoch time: 47.81 s
2025-01-20 16:33:33.067954: Yayy! New best EMA pseudo Dice: 0.7778000235557556
2025-01-20 16:33:33.916121:
2025-01-20 16:33:33.951462: Epoch 74
2025-01-20 16:33:33.951548: Current learning rate: 0.00933
2025-01-20 16:34:21.745415: train_loss -0.6925
2025-01-20 16:34:21.780573: val_loss -0.6723
2025-01-20 16:34:21.780628: Pseudo dice [np.float32(0.737), np.float32(0.7311), np.float32(0.8498), np.float32(0.7121), np.float32(0.8869), np.float32(0.754)]
2025-01-20 16:34:21.780663: Epoch time: 47.83 s
2025-01-20 16:34:21.780683: Yayy! New best EMA pseudo Dice: 0.7778000235557556
2025-01-20 16:34:22.636310:
2025-01-20 16:34:22.671685: Epoch 75
2025-01-20 16:34:22.671778: Current learning rate: 0.00932
2025-01-20 16:35:10.442775: train_loss -0.6848
2025-01-20 16:35:10.442958: val_loss -0.681
2025-01-20 16:35:10.443001: Pseudo dice [np.float32(0.725), np.float32(0.7542), np.float32(0.8373), np.float32(0.7281), np.float32(0.8826), np.float32(0.7659)]
2025-01-20 16:35:10.443040: Epoch time: 47.81 s
2025-01-20 16:35:10.443060: Yayy! New best EMA pseudo Dice: 0.7782999873161316
2025-01-20 16:35:11.306860:
2025-01-20 16:35:11.307739: Epoch 76
2025-01-20 16:35:11.307801: Current learning rate: 0.00931
2025-01-20 16:35:59.119850: train_loss -0.684
2025-01-20 16:35:59.155079: val_loss -0.6672
2025-01-20 16:35:59.155168: Pseudo dice [np.float32(0.7429), np.float32(0.7371), np.float32(0.8404), np.float32(0.7344), np.float32(0.8694), np.float32(0.7548)]
2025-01-20 16:35:59.155204: Epoch time: 47.81 s
2025-01-20 16:35:59.155225: Yayy! New best EMA pseudo Dice: 0.7784000039100647
2025-01-20 16:35:59.998966:
2025-01-20 16:36:00.034276: Epoch 77
2025-01-20 16:36:00.034386: Current learning rate: 0.0093
2025-01-20 16:36:47.846853: train_loss -0.683
2025-01-20 16:36:47.847071: val_loss -0.6595
2025-01-20 16:36:47.847118: Pseudo dice [np.float32(0.7333), np.float32(0.7664), np.float32(0.8479), np.float32(0.681), np.float32(0.8773), np.float32(0.7594)]
2025-01-20 16:36:47.847155: Epoch time: 47.85 s
2025-01-20 16:36:48.315140:
2025-01-20 16:36:48.349576: Epoch 78
2025-01-20 16:36:48.349643: Current learning rate: 0.0093
2025-01-20 16:37:36.137221: train_loss -0.7001
2025-01-20 16:37:36.172298: val_loss -0.6835
2025-01-20 16:37:36.172361: Pseudo dice [np.float32(0.7526), np.float32(0.7613), np.float32(0.8514), np.float32(0.7249), np.float32(0.8848), np.float32(0.7687)]
2025-01-20 16:37:36.172405: Epoch time: 47.82 s
2025-01-20 16:37:36.172432: Yayy! New best EMA pseudo Dice: 0.7796000242233276
2025-01-20 16:37:37.019365:
2025-01-20 16:37:37.022741: Epoch 79
2025-01-20 16:37:37.022834: Current learning rate: 0.00929
2025-01-20 16:38:24.779684: train_loss -0.6846
2025-01-20 16:38:24.814767: val_loss -0.6699
2025-01-20 16:38:24.814852: Pseudo dice [np.float32(0.7397), np.float32(0.7339), np.float32(0.8435), np.float32(0.6484), np.float32(0.8819), np.float32(0.7534)]
2025-01-20 16:38:24.814889: Epoch time: 47.76 s
2025-01-20 16:38:25.284203:
2025-01-20 16:38:25.318651: Epoch 80
2025-01-20 16:38:25.318719: Current learning rate: 0.00928
2025-01-20 16:39:13.101423: train_loss -0.6916
2025-01-20 16:39:13.136588: val_loss -0.6906
2025-01-20 16:39:13.136646: Pseudo dice [np.float32(0.7441), np.float32(0.7365), np.float32(0.8559), np.float32(0.7399), np.float32(0.8687), np.float32(0.7571)]
2025-01-20 16:39:13.136683: Epoch time: 47.82 s
2025-01-20 16:39:13.726582:
2025-01-20 16:39:13.761030: Epoch 81
2025-01-20 16:39:13.761106: Current learning rate: 0.00927
2025-01-20 16:40:01.573131: train_loss -0.6858
2025-01-20 16:40:01.608125: val_loss -0.6853
2025-01-20 16:40:01.608181: Pseudo dice [np.float32(0.7502), np.float32(0.7536), np.float32(0.8594), np.float32(0.751), np.float32(0.8836), np.float32(0.7716)]
2025-01-20 16:40:01.608218: Epoch time: 47.85 s
2025-01-20 16:40:01.608240: Yayy! New best EMA pseudo Dice: 0.7803999781608582
2025-01-20 16:40:02.463320:
2025-01-20 16:40:02.498608: Epoch 82
2025-01-20 16:40:02.498693: Current learning rate: 0.00926
2025-01-20 16:40:50.268087: train_loss -0.6919
2025-01-20 16:40:50.303180: val_loss -0.6949
2025-01-20 16:40:50.303236: Pseudo dice [np.float32(0.7496), np.float32(0.7511), np.float32(0.8587), np.float32(0.7276), np.float32(0.894), np.float32(0.764)]
2025-01-20 16:40:50.303273: Epoch time: 47.81 s
2025-01-20 16:40:50.303293: Yayy! New best EMA pseudo Dice: 0.781499981880188
2025-01-20 16:40:51.148641:
2025-01-20 16:40:51.183906: Epoch 83
2025-01-20 16:40:51.183968: Current learning rate: 0.00925
2025-01-20 16:41:38.974570: train_loss -0.689
2025-01-20 16:41:39.009701: val_loss -0.6928
2025-01-20 16:41:39.009769: Pseudo dice [np.float32(0.7355), np.float32(0.7693), np.float32(0.8445), np.float32(0.7215), np.float32(0.8825), np.float32(0.7651)]
2025-01-20 16:41:39.009811: Epoch time: 47.83 s
2025-01-20 16:41:39.009832: Yayy! New best EMA pseudo Dice: 0.7820000052452087
2025-01-20 16:41:39.848565:
2025-01-20 16:41:39.848637: Epoch 84
2025-01-20 16:41:39.848695: Current learning rate: 0.00924
2025-01-20 16:42:27.615632: train_loss -0.6831
2025-01-20 16:42:27.650852: val_loss -0.6664
2025-01-20 16:42:27.650911: Pseudo dice [np.float32(0.7342), np.float32(0.732), np.float32(0.8429), np.float32(0.6954), np.float32(0.8715), np.float32(0.7679)]
2025-01-20 16:42:27.650949: Epoch time: 47.77 s
2025-01-20 16:42:28.106931:
2025-01-20 16:42:28.141633: Epoch 85
2025-01-20 16:42:28.141695: Current learning rate: 0.00923
2025-01-20 16:43:15.953081: train_loss -0.6816
2025-01-20 16:43:16.007538: val_loss -0.6657
2025-01-20 16:43:16.007593: Pseudo dice [np.float32(0.7365), np.float32(0.7328), np.float32(0.8476), np.float32(0.7117), np.float32(0.8588), np.float32(0.7248)]