Datasets:
ngaggion
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ChronoRoot_nnUNet

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2025-01-20 17:05:05.623155:
2025-01-20 17:05:05.658512: Epoch 113
2025-01-20 17:05:05.658602: Current learning rate: 0.00898
2025-01-20 17:05:53.451125: train_loss -0.6913
2025-01-20 17:05:53.486128: val_loss -0.6869
2025-01-20 17:05:53.486201: Pseudo dice [np.float32(0.7343), np.float32(0.7644), np.float32(0.8611), np.float32(0.6819), np.float32(0.8885), np.float32(0.7755)]
2025-01-20 17:05:53.486260: Epoch time: 47.83 s
2025-01-20 17:05:54.052606:
2025-01-20 17:05:54.087133: Epoch 114
2025-01-20 17:05:54.087213: Current learning rate: 0.00897
2025-01-20 17:06:41.906235: train_loss -0.7028
2025-01-20 17:06:41.906421: val_loss -0.679
2025-01-20 17:06:41.906474: Pseudo dice [np.float32(0.7457), np.float32(0.7471), np.float32(0.8551), np.float32(0.7215), np.float32(0.8815), np.float32(0.7783)]
2025-01-20 17:06:41.906521: Epoch time: 47.85 s
2025-01-20 17:06:42.367122:
2025-01-20 17:06:42.401572: Epoch 115
2025-01-20 17:06:42.401645: Current learning rate: 0.00896
2025-01-20 17:07:30.210726: train_loss -0.6841
2025-01-20 17:07:30.245838: val_loss -0.7021
2025-01-20 17:07:30.245907: Pseudo dice [np.float32(0.7404), np.float32(0.7186), np.float32(0.8579), np.float32(0.7546), np.float32(0.8975), np.float32(0.7812)]
2025-01-20 17:07:30.245942: Epoch time: 47.84 s
2025-01-20 17:07:30.245968: Yayy! New best EMA pseudo Dice: 0.7878999710083008
2025-01-20 17:07:31.092018:
2025-01-20 17:07:31.094494: Epoch 116
2025-01-20 17:07:31.094584: Current learning rate: 0.00895
2025-01-20 17:08:18.887552: train_loss -0.6957
2025-01-20 17:08:18.922638: val_loss -0.6911
2025-01-20 17:08:18.922717: Pseudo dice [np.float32(0.7555), np.float32(0.7278), np.float32(0.8387), np.float32(0.7434), np.float32(0.8814), np.float32(0.7634)]
2025-01-20 17:08:18.922753: Epoch time: 47.8 s
2025-01-20 17:08:19.385820:
2025-01-20 17:08:19.420279: Epoch 117
2025-01-20 17:08:19.420340: Current learning rate: 0.00894
2025-01-20 17:09:07.221681: train_loss -0.6919
2025-01-20 17:09:07.256821: val_loss -0.7113
2025-01-20 17:09:07.256876: Pseudo dice [np.float32(0.7417), np.float32(0.7463), np.float32(0.8605), np.float32(0.7423), np.float32(0.8916), np.float32(0.784)]
2025-01-20 17:09:07.256914: Epoch time: 47.84 s
2025-01-20 17:09:07.256935: Yayy! New best EMA pseudo Dice: 0.7882999777793884
2025-01-20 17:09:08.100840:
2025-01-20 17:09:08.136190: Epoch 118
2025-01-20 17:09:08.136288: Current learning rate: 0.00893
2025-01-20 17:09:55.924616: train_loss -0.6902
2025-01-20 17:09:55.959728: val_loss -0.6781
2025-01-20 17:09:55.959783: Pseudo dice [np.float32(0.7449), np.float32(0.7469), np.float32(0.8463), np.float32(0.7254), np.float32(0.8781), np.float32(0.7732)]
2025-01-20 17:09:55.959822: Epoch time: 47.82 s
2025-01-20 17:09:56.421459:
2025-01-20 17:09:56.455946: Epoch 119
2025-01-20 17:09:56.456007: Current learning rate: 0.00892
2025-01-20 17:10:44.204669: train_loss -0.6924
2025-01-20 17:10:44.239789: val_loss -0.6992
2025-01-20 17:10:44.239845: Pseudo dice [np.float32(0.749), np.float32(0.7383), np.float32(0.8427), np.float32(0.7363), np.float32(0.8856), np.float32(0.7738)]
2025-01-20 17:10:44.239881: Epoch time: 47.78 s
2025-01-20 17:10:44.702236:
2025-01-20 17:10:44.736727: Epoch 120
2025-01-20 17:10:44.736816: Current learning rate: 0.00891
2025-01-20 17:11:32.489763: train_loss -0.6941
2025-01-20 17:11:32.524882: val_loss -0.6872
2025-01-20 17:11:32.524937: Pseudo dice [np.float32(0.7399), np.float32(0.7317), np.float32(0.8527), np.float32(0.7222), np.float32(0.8927), np.float32(0.7678)]
2025-01-20 17:11:32.524974: Epoch time: 47.79 s
2025-01-20 17:11:32.987600:
2025-01-20 17:11:33.022087: Epoch 121
2025-01-20 17:11:33.022150: Current learning rate: 0.0089
2025-01-20 17:12:20.802694: train_loss -0.6871
2025-01-20 17:12:20.837830: val_loss -0.6986
2025-01-20 17:12:20.837885: Pseudo dice [np.float32(0.7449), np.float32(0.7685), np.float32(0.8461), np.float32(0.7499), np.float32(0.8838), np.float32(0.7743)]
2025-01-20 17:12:20.837922: Epoch time: 47.82 s
2025-01-20 17:12:20.837943: Yayy! New best EMA pseudo Dice: 0.7882999777793884
2025-01-20 17:12:21.797801:
2025-01-20 17:12:21.799222: Epoch 122
2025-01-20 17:12:21.799308: Current learning rate: 0.00889
2025-01-20 17:13:09.621096: train_loss -0.6953
2025-01-20 17:13:09.656283: val_loss -0.6939
2025-01-20 17:13:09.656336: Pseudo dice [np.float32(0.7436), np.float32(0.7497), np.float32(0.8558), np.float32(0.7452), np.float32(0.8838), np.float32(0.7705)]
2025-01-20 17:13:09.656372: Epoch time: 47.82 s
2025-01-20 17:13:09.656393: Yayy! New best EMA pseudo Dice: 0.7886999845504761
2025-01-20 17:13:10.502504:
2025-01-20 17:13:10.537771: Epoch 123
2025-01-20 17:13:10.537875: Current learning rate: 0.00889
2025-01-20 17:13:58.337601: train_loss -0.6976
2025-01-20 17:13:58.372732: val_loss -0.6878
2025-01-20 17:13:58.372793: Pseudo dice [np.float32(0.7501), np.float32(0.7612), np.float32(0.8464), np.float32(0.7281), np.float32(0.8791), np.float32(0.7707)]
2025-01-20 17:13:58.372839: Epoch time: 47.84 s
2025-01-20 17:13:58.372868: Yayy! New best EMA pseudo Dice: 0.7886999845504761
2025-01-20 17:13:59.223355:
2025-01-20 17:13:59.226215: Epoch 124
2025-01-20 17:13:59.226315: Current learning rate: 0.00888
2025-01-20 17:14:47.028013: train_loss -0.6977
2025-01-20 17:14:47.063116: val_loss -0.6818
2025-01-20 17:14:47.063178: Pseudo dice [np.float32(0.7504), np.float32(0.7594), np.float32(0.8578), np.float32(0.71), np.float32(0.8757), np.float32(0.7656)]
2025-01-20 17:14:47.063230: Epoch time: 47.81 s
2025-01-20 17:14:47.527092:
2025-01-20 17:14:47.561540: Epoch 125
2025-01-20 17:14:47.561612: Current learning rate: 0.00887
2025-01-20 17:15:35.353159: train_loss -0.7012
2025-01-20 17:15:35.388139: val_loss -0.7011
2025-01-20 17:15:35.388223: Pseudo dice [np.float32(0.7475), np.float32(0.7538), np.float32(0.8484), np.float32(0.7652), np.float32(0.8898), np.float32(0.7955)]
2025-01-20 17:15:35.388271: Epoch time: 47.83 s
2025-01-20 17:15:35.388298: Yayy! New best EMA pseudo Dice: 0.7896999716758728
2025-01-20 17:15:36.237062:
2025-01-20 17:15:36.272432: Epoch 126
2025-01-20 17:15:36.272522: Current learning rate: 0.00886