Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 17:16:24.073946: train_loss -0.7072
2025-01-20 17:16:24.109100: val_loss -0.6769
2025-01-20 17:16:24.109171: Pseudo dice [np.float32(0.7632), np.float32(0.7683), np.float32(0.8482), np.float32(0.7083), np.float32(0.889), np.float32(0.7655)]
2025-01-20 17:16:24.109206: Epoch time: 47.84 s
2025-01-20 17:16:24.109228: Yayy! New best EMA pseudo Dice: 0.7896999716758728
2025-01-20 17:16:24.955017:
2025-01-20 17:16:24.956239: Epoch 127
2025-01-20 17:16:24.956313: Current learning rate: 0.00885
2025-01-20 17:17:12.773409: train_loss -0.7044
2025-01-20 17:17:12.808588: val_loss -0.7147
2025-01-20 17:17:12.808644: Pseudo dice [np.float32(0.7536), np.float32(0.7605), np.float32(0.8591), np.float32(0.7523), np.float32(0.9047), np.float32(0.7701)]
2025-01-20 17:17:12.808725: Epoch time: 47.82 s
2025-01-20 17:17:12.808747: Yayy! New best EMA pseudo Dice: 0.7907999753952026
2025-01-20 17:17:13.660082:
2025-01-20 17:17:13.695249: Epoch 128
2025-01-20 17:17:13.695342: Current learning rate: 0.00884
2025-01-20 17:18:01.502681: train_loss -0.6958
2025-01-20 17:18:01.537819: val_loss -0.6847
2025-01-20 17:18:01.537874: Pseudo dice [np.float32(0.7477), np.float32(0.7768), np.float32(0.849), np.float32(0.7084), np.float32(0.873), np.float32(0.7556)]
2025-01-20 17:18:01.537911: Epoch time: 47.84 s
2025-01-20 17:18:02.002640:
2025-01-20 17:18:02.037055: Epoch 129
2025-01-20 17:18:02.037159: Current learning rate: 0.00883
2025-01-20 17:18:49.826287: train_loss -0.6875
2025-01-20 17:18:49.861349: val_loss -0.683
2025-01-20 17:18:49.861415: Pseudo dice [np.float32(0.746), np.float32(0.7683), np.float32(0.8558), np.float32(0.7384), np.float32(0.8923), np.float32(0.7785)]
2025-01-20 17:18:49.861468: Epoch time: 47.82 s
2025-01-20 17:18:49.861501: Yayy! New best EMA pseudo Dice: 0.7907999753952026
2025-01-20 17:18:50.818861:
2025-01-20 17:18:50.821919: Epoch 130
2025-01-20 17:18:50.821985: Current learning rate: 0.00882
2025-01-20 17:19:38.655999: train_loss -0.7076
2025-01-20 17:19:38.691193: val_loss -0.7065
2025-01-20 17:19:38.691263: Pseudo dice [np.float32(0.7381), np.float32(0.7582), np.float32(0.8545), np.float32(0.7324), np.float32(0.8878), np.float32(0.7836)]
2025-01-20 17:19:38.691306: Epoch time: 47.84 s
2025-01-20 17:19:38.691328: Yayy! New best EMA pseudo Dice: 0.7910000085830688
2025-01-20 17:19:39.535866:
2025-01-20 17:19:39.571107: Epoch 131
2025-01-20 17:19:39.571198: Current learning rate: 0.00881
2025-01-20 17:20:27.436798: train_loss -0.6939
2025-01-20 17:20:27.471892: val_loss -0.7141
2025-01-20 17:20:27.471972: Pseudo dice [np.float32(0.7563), np.float32(0.7761), np.float32(0.8418), np.float32(0.7602), np.float32(0.8781), np.float32(0.7686)]
2025-01-20 17:20:27.472009: Epoch time: 47.9 s
2025-01-20 17:20:27.472030: Yayy! New best EMA pseudo Dice: 0.7915999889373779
2025-01-20 17:20:28.321473:
2025-01-20 17:20:28.325720: Epoch 132
2025-01-20 17:20:28.325807: Current learning rate: 0.0088
2025-01-20 17:21:16.132462: train_loss -0.7043
2025-01-20 17:21:16.167632: val_loss -0.6856
2025-01-20 17:21:16.167725: Pseudo dice [np.float32(0.7469), np.float32(0.7461), np.float32(0.8509), np.float32(0.7277), np.float32(0.8898), np.float32(0.7486)]
2025-01-20 17:21:16.167777: Epoch time: 47.81 s
2025-01-20 17:21:16.639035:
2025-01-20 17:21:16.673472: Epoch 133
2025-01-20 17:21:16.673534: Current learning rate: 0.00879
2025-01-20 17:22:04.659590: train_loss -0.7036
2025-01-20 17:22:04.694734: val_loss -0.697
2025-01-20 17:22:04.694790: Pseudo dice [np.float32(0.7527), np.float32(0.76), np.float32(0.8528), np.float32(0.7571), np.float32(0.8744), np.float32(0.769)]
2025-01-20 17:22:04.694827: Epoch time: 48.02 s
2025-01-20 17:22:05.161073:
2025-01-20 17:22:05.195532: Epoch 134
2025-01-20 17:22:05.195623: Current learning rate: 0.00879
2025-01-20 17:22:53.420521: train_loss -0.6974
2025-01-20 17:22:53.455733: val_loss -0.6852
2025-01-20 17:22:53.455826: Pseudo dice [np.float32(0.7349), np.float32(0.721), np.float32(0.8506), np.float32(0.7352), np.float32(0.8839), np.float32(0.7704)]
2025-01-20 17:22:53.455889: Epoch time: 48.26 s
2025-01-20 17:22:53.923641:
2025-01-20 17:22:53.958072: Epoch 135
2025-01-20 17:22:53.958138: Current learning rate: 0.00878
2025-01-20 17:23:41.947998: train_loss -0.7069
2025-01-20 17:23:41.983114: val_loss -0.6959
2025-01-20 17:23:41.983168: Pseudo dice [np.float32(0.7591), np.float32(0.7479), np.float32(0.8395), np.float32(0.7391), np.float32(0.8911), np.float32(0.7722)]
2025-01-20 17:23:41.983205: Epoch time: 48.02 s
2025-01-20 17:23:42.450922:
2025-01-20 17:23:42.485310: Epoch 136
2025-01-20 17:23:42.485403: Current learning rate: 0.00877
2025-01-20 17:24:30.324975: train_loss -0.7104
2025-01-20 17:24:30.359964: val_loss -0.6974
2025-01-20 17:24:30.360019: Pseudo dice [np.float32(0.7685), np.float32(0.7837), np.float32(0.8462), np.float32(0.7221), np.float32(0.8869), np.float32(0.7799)]
2025-01-20 17:24:30.360061: Epoch time: 47.87 s
2025-01-20 17:24:30.829644:
2025-01-20 17:24:30.829699: Epoch 137
2025-01-20 17:24:30.829785: Current learning rate: 0.00876
2025-01-20 17:25:18.663806: train_loss -0.6931
2025-01-20 17:25:18.698979: val_loss -0.6915
2025-01-20 17:25:18.699034: Pseudo dice [np.float32(0.7418), np.float32(0.7832), np.float32(0.8375), np.float32(0.6827), np.float32(0.8775), np.float32(0.7626)]
2025-01-20 17:25:18.699074: Epoch time: 47.83 s
2025-01-20 17:25:19.279663:
2025-01-20 17:25:19.314176: Epoch 138
2025-01-20 17:25:19.314244: Current learning rate: 0.00875
2025-01-20 17:26:07.185118: train_loss -0.6899
2025-01-20 17:26:07.220186: val_loss -0.7131
2025-01-20 17:26:07.220277: Pseudo dice [np.float32(0.7541), np.float32(0.7618), np.float32(0.8558), np.float32(0.7512), np.float32(0.8971), np.float32(0.7964)]
2025-01-20 17:26:07.220314: Epoch time: 47.91 s
2025-01-20 17:26:07.701285:
2025-01-20 17:26:07.735769: Epoch 139
2025-01-20 17:26:07.735862: Current learning rate: 0.00874
2025-01-20 17:26:55.603769: train_loss -0.7044
2025-01-20 17:26:55.638874: val_loss -0.6852
2025-01-20 17:26:55.638931: Pseudo dice [np.float32(0.7469), np.float32(0.7409), np.float32(0.8512), np.float32(0.7382), np.float32(0.878), np.float32(0.761)]
2025-01-20 17:26:55.638968: Epoch time: 47.9 s