Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 16:43:16.007630: Epoch time: 47.85 s
2025-01-20 16:43:16.466988:
2025-01-20 16:43:16.467044: Epoch 86
2025-01-20 16:43:16.467100: Current learning rate: 0.00922
2025-01-20 16:44:04.288815: train_loss -0.6797
2025-01-20 16:44:04.323926: val_loss -0.6736
2025-01-20 16:44:04.323984: Pseudo dice [np.float32(0.7434), np.float32(0.7298), np.float32(0.8532), np.float32(0.7158), np.float32(0.8605), np.float32(0.7603)]
2025-01-20 16:44:04.324023: Epoch time: 47.82 s
2025-01-20 16:44:04.782981:
2025-01-20 16:44:04.817451: Epoch 87
2025-01-20 16:44:04.817514: Current learning rate: 0.00921
2025-01-20 16:44:52.595650: train_loss -0.6872
2025-01-20 16:44:52.630769: val_loss -0.6751
2025-01-20 16:44:52.630829: Pseudo dice [np.float32(0.735), np.float32(0.7626), np.float32(0.8544), np.float32(0.7236), np.float32(0.8814), np.float32(0.7689)]
2025-01-20 16:44:52.630865: Epoch time: 47.81 s
2025-01-20 16:44:53.089869:
2025-01-20 16:44:53.124341: Epoch 88
2025-01-20 16:44:53.124428: Current learning rate: 0.0092
2025-01-20 16:45:40.923224: train_loss -0.6816
2025-01-20 16:45:40.958369: val_loss -0.6712
2025-01-20 16:45:40.958434: Pseudo dice [np.float32(0.7458), np.float32(0.7566), np.float32(0.8505), np.float32(0.7116), np.float32(0.8709), np.float32(0.7618)]
2025-01-20 16:45:40.958485: Epoch time: 47.83 s
2025-01-20 16:45:41.527789:
2025-01-20 16:45:41.562265: Epoch 89
2025-01-20 16:45:41.562328: Current learning rate: 0.0092
2025-01-20 16:46:29.392438: train_loss -0.6963
2025-01-20 16:46:29.427605: val_loss -0.7152
2025-01-20 16:46:29.427669: Pseudo dice [np.float32(0.7536), np.float32(0.7795), np.float32(0.8639), np.float32(0.736), np.float32(0.888), np.float32(0.786)]
2025-01-20 16:46:29.427728: Epoch time: 47.87 s
2025-01-20 16:46:29.427751: Yayy! New best EMA pseudo Dice: 0.7827000021934509
2025-01-20 16:46:30.267154:
2025-01-20 16:46:30.302513: Epoch 90
2025-01-20 16:46:30.302609: Current learning rate: 0.00919
2025-01-20 16:47:18.105514: train_loss -0.6818
2025-01-20 16:47:18.140665: val_loss -0.6837
2025-01-20 16:47:18.140740: Pseudo dice [np.float32(0.7383), np.float32(0.7157), np.float32(0.846), np.float32(0.7336), np.float32(0.8703), np.float32(0.7699)]
2025-01-20 16:47:18.140780: Epoch time: 47.84 s
2025-01-20 16:47:18.600095:
2025-01-20 16:47:18.634599: Epoch 91
2025-01-20 16:47:18.634679: Current learning rate: 0.00918
2025-01-20 16:48:06.462476: train_loss -0.6782
2025-01-20 16:48:06.497616: val_loss -0.6853
2025-01-20 16:48:06.497689: Pseudo dice [np.float32(0.7412), np.float32(0.7415), np.float32(0.8532), np.float32(0.7226), np.float32(0.8903), np.float32(0.7599)]
2025-01-20 16:48:06.497743: Epoch time: 47.86 s
2025-01-20 16:48:06.949394:
2025-01-20 16:48:06.983858: Epoch 92
2025-01-20 16:48:06.983932: Current learning rate: 0.00917
2025-01-20 16:48:54.812181: train_loss -0.6843
2025-01-20 16:48:54.847356: val_loss -0.7014
2025-01-20 16:48:54.847411: Pseudo dice [np.float32(0.7382), np.float32(0.7202), np.float32(0.8442), np.float32(0.7372), np.float32(0.8989), np.float32(0.7731)]
2025-01-20 16:48:54.847448: Epoch time: 47.86 s
2025-01-20 16:48:54.847473: Yayy! New best EMA pseudo Dice: 0.7828999757766724
2025-01-20 16:48:55.675326:
2025-01-20 16:48:55.678985: Epoch 93
2025-01-20 16:48:55.679078: Current learning rate: 0.00916
2025-01-20 16:49:43.473017: train_loss -0.6966
2025-01-20 16:49:43.473100: val_loss -0.6856
2025-01-20 16:49:43.473161: Pseudo dice [np.float32(0.7442), np.float32(0.7409), np.float32(0.8447), np.float32(0.7052), np.float32(0.8732), np.float32(0.7542)]
2025-01-20 16:49:43.473199: Epoch time: 47.8 s
2025-01-20 16:49:43.927982:
2025-01-20 16:49:43.962438: Epoch 94
2025-01-20 16:49:43.962512: Current learning rate: 0.00915
2025-01-20 16:50:31.759556: train_loss -0.6833
2025-01-20 16:50:31.794538: val_loss -0.6784
2025-01-20 16:50:31.794614: Pseudo dice [np.float32(0.7466), np.float32(0.7731), np.float32(0.8484), np.float32(0.7141), np.float32(0.8891), np.float32(0.7731)]
2025-01-20 16:50:31.794654: Epoch time: 47.83 s
2025-01-20 16:50:31.794692: Yayy! New best EMA pseudo Dice: 0.7831000089645386
2025-01-20 16:50:32.623553:
2025-01-20 16:50:32.658937: Epoch 95
2025-01-20 16:50:32.659011: Current learning rate: 0.00914
2025-01-20 16:51:20.434032: train_loss -0.6843
2025-01-20 16:51:20.469011: val_loss -0.7095
2025-01-20 16:51:20.469069: Pseudo dice [np.float32(0.7578), np.float32(0.7799), np.float32(0.852), np.float32(0.7592), np.float32(0.8867), np.float32(0.7682)]
2025-01-20 16:51:20.469112: Epoch time: 47.81 s
2025-01-20 16:51:20.469136: Yayy! New best EMA pseudo Dice: 0.7849000096321106
2025-01-20 16:51:21.303999:
2025-01-20 16:51:21.307483: Epoch 96
2025-01-20 16:51:21.307572: Current learning rate: 0.00913
2025-01-20 16:52:09.088717: train_loss -0.6971
2025-01-20 16:52:09.123897: val_loss -0.6804
2025-01-20 16:52:09.123977: Pseudo dice [np.float32(0.7527), np.float32(0.7355), np.float32(0.8595), np.float32(0.723), np.float32(0.8879), np.float32(0.7715)]
2025-01-20 16:52:09.124016: Epoch time: 47.79 s
2025-01-20 16:52:09.124038: Yayy! New best EMA pseudo Dice: 0.7851999998092651
2025-01-20 16:52:10.071998:
2025-01-20 16:52:10.107299: Epoch 97
2025-01-20 16:52:10.107398: Current learning rate: 0.00912
2025-01-20 16:52:57.923171: train_loss -0.6746
2025-01-20 16:52:57.958202: val_loss -0.6775
2025-01-20 16:52:57.958282: Pseudo dice [np.float32(0.7317), np.float32(0.7433), np.float32(0.846), np.float32(0.6734), np.float32(0.855), np.float32(0.7419)]
2025-01-20 16:52:57.958319: Epoch time: 47.85 s
2025-01-20 16:52:58.414899:
2025-01-20 16:52:58.449271: Epoch 98
2025-01-20 16:52:58.449335: Current learning rate: 0.00911
2025-01-20 16:53:46.262128: train_loss -0.6766
2025-01-20 16:53:46.297303: val_loss -0.6692
2025-01-20 16:53:46.297367: Pseudo dice [np.float32(0.7393), np.float32(0.7539), np.float32(0.8336), np.float32(0.7064), np.float32(0.8636), np.float32(0.756)]
2025-01-20 16:53:46.297409: Epoch time: 47.85 s
2025-01-20 16:53:46.754996:
2025-01-20 16:53:46.789483: Epoch 99
2025-01-20 16:53:46.789561: Current learning rate: 0.0091