Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 20:04:53.584947: train_loss -0.7237
2025-01-20 20:04:53.620021: val_loss -0.716
2025-01-20 20:04:53.620092: Pseudo dice [np.float32(0.7719), np.float32(0.7736), np.float32(0.8672), np.float32(0.7644), np.float32(0.8859), np.float32(0.7834)]
2025-01-20 20:04:53.620142: Epoch time: 47.67 s
2025-01-20 20:04:54.098903:
2025-01-20 20:04:54.133482: Epoch 336
2025-01-20 20:04:54.133565: Current learning rate: 0.00692
2025-01-20 20:05:41.759375: train_loss -0.7359
2025-01-20 20:05:41.794518: val_loss -0.7184
2025-01-20 20:05:41.794573: Pseudo dice [np.float32(0.7677), np.float32(0.7728), np.float32(0.8673), np.float32(0.7392), np.float32(0.8929), np.float32(0.7569)]
2025-01-20 20:05:41.794609: Epoch time: 47.66 s
2025-01-20 20:05:42.262690:
2025-01-20 20:05:42.297187: Epoch 337
2025-01-20 20:05:42.297249: Current learning rate: 0.00691
2025-01-20 20:06:29.962896: train_loss -0.7171
2025-01-20 20:06:29.998047: val_loss -0.7173
2025-01-20 20:06:29.998106: Pseudo dice [np.float32(0.7604), np.float32(0.7682), np.float32(0.8597), np.float32(0.7389), np.float32(0.9026), np.float32(0.7864)]
2025-01-20 20:06:29.998157: Epoch time: 47.7 s
2025-01-20 20:06:30.467599:
2025-01-20 20:06:30.502103: Epoch 338
2025-01-20 20:06:30.502197: Current learning rate: 0.0069
2025-01-20 20:07:18.143001: train_loss -0.7125
2025-01-20 20:07:18.178185: val_loss -0.7251
2025-01-20 20:07:18.178242: Pseudo dice [np.float32(0.7544), np.float32(0.7741), np.float32(0.8681), np.float32(0.7421), np.float32(0.9106), np.float32(0.807)]
2025-01-20 20:07:18.178285: Epoch time: 47.68 s
2025-01-20 20:07:18.646015:
2025-01-20 20:07:18.680491: Epoch 339
2025-01-20 20:07:18.680554: Current learning rate: 0.00689
2025-01-20 20:08:06.324167: train_loss -0.7185
2025-01-20 20:08:06.359289: val_loss -0.6893
2025-01-20 20:08:06.359344: Pseudo dice [np.float32(0.7477), np.float32(0.7199), np.float32(0.8593), np.float32(0.7264), np.float32(0.8883), np.float32(0.7701)]
2025-01-20 20:08:06.359381: Epoch time: 47.68 s
2025-01-20 20:08:06.961393:
2025-01-20 20:08:06.995837: Epoch 340
2025-01-20 20:08:06.995919: Current learning rate: 0.00688
2025-01-20 20:08:54.682868: train_loss -0.7167
2025-01-20 20:08:54.718023: val_loss -0.7267
2025-01-20 20:08:54.718087: Pseudo dice [np.float32(0.7596), np.float32(0.7715), np.float32(0.8604), np.float32(0.7662), np.float32(0.8985), np.float32(0.7907)]
2025-01-20 20:08:54.718130: Epoch time: 47.72 s
2025-01-20 20:08:55.194353:
2025-01-20 20:08:55.228811: Epoch 341
2025-01-20 20:08:55.228881: Current learning rate: 0.00687
2025-01-20 20:09:42.892975: train_loss -0.7164
2025-01-20 20:09:42.928075: val_loss -0.7121
2025-01-20 20:09:42.928129: Pseudo dice [np.float32(0.7382), np.float32(0.7554), np.float32(0.8568), np.float32(0.7332), np.float32(0.8844), np.float32(0.7798)]
2025-01-20 20:09:42.928165: Epoch time: 47.7 s
2025-01-20 20:09:43.399159:
2025-01-20 20:09:43.433624: Epoch 342
2025-01-20 20:09:43.433684: Current learning rate: 0.00686
2025-01-20 20:10:31.078751: train_loss -0.7258
2025-01-20 20:10:31.113856: val_loss -0.7017
2025-01-20 20:10:31.113925: Pseudo dice [np.float32(0.7647), np.float32(0.7467), np.float32(0.8678), np.float32(0.7195), np.float32(0.8879), np.float32(0.7723)]
2025-01-20 20:10:31.113988: Epoch time: 47.68 s
2025-01-20 20:10:31.580845:
2025-01-20 20:10:31.615222: Epoch 343
2025-01-20 20:10:31.615309: Current learning rate: 0.00685
2025-01-20 20:11:19.243160: train_loss -0.7278
2025-01-20 20:11:19.278158: val_loss -0.7297
2025-01-20 20:11:19.278223: Pseudo dice [np.float32(0.7729), np.float32(0.7686), np.float32(0.8582), np.float32(0.7594), np.float32(0.898), np.float32(0.8018)]
2025-01-20 20:11:19.278283: Epoch time: 47.66 s
2025-01-20 20:11:19.748137:
2025-01-20 20:11:19.782633: Epoch 344
2025-01-20 20:11:19.782730: Current learning rate: 0.00684
2025-01-20 20:12:07.425356: train_loss -0.7272
2025-01-20 20:12:07.460467: val_loss -0.7358
2025-01-20 20:12:07.460541: Pseudo dice [np.float32(0.766), np.float32(0.7543), np.float32(0.8644), np.float32(0.761), np.float32(0.9035), np.float32(0.8011)]
2025-01-20 20:12:07.460612: Epoch time: 47.68 s
2025-01-20 20:12:07.932965:
2025-01-20 20:12:07.967402: Epoch 345
2025-01-20 20:12:07.967478: Current learning rate: 0.00683
2025-01-20 20:12:55.643568: train_loss -0.7251
2025-01-20 20:12:55.678667: val_loss -0.6927
2025-01-20 20:12:55.678741: Pseudo dice [np.float32(0.7639), np.float32(0.7768), np.float32(0.8626), np.float32(0.7318), np.float32(0.8741), np.float32(0.7541)]
2025-01-20 20:12:55.678781: Epoch time: 47.71 s
2025-01-20 20:12:56.150827:
2025-01-20 20:12:56.185236: Epoch 346
2025-01-20 20:12:56.185299: Current learning rate: 0.00682
2025-01-20 20:13:43.824499: train_loss -0.7163
2025-01-20 20:13:43.859475: val_loss -0.7055
2025-01-20 20:13:43.859539: Pseudo dice [np.float32(0.7621), np.float32(0.7528), np.float32(0.8551), np.float32(0.757), np.float32(0.8847), np.float32(0.7932)]
2025-01-20 20:13:43.859607: Epoch time: 47.67 s
2025-01-20 20:13:44.435314:
2025-01-20 20:13:44.469781: Epoch 347
2025-01-20 20:13:44.469848: Current learning rate: 0.00681
2025-01-20 20:14:32.152784: train_loss -0.7288
2025-01-20 20:14:32.187905: val_loss -0.7014
2025-01-20 20:14:32.187960: Pseudo dice [np.float32(0.7628), np.float32(0.7635), np.float32(0.868), np.float32(0.7427), np.float32(0.8947), np.float32(0.7796)]
2025-01-20 20:14:32.187996: Epoch time: 47.72 s
2025-01-20 20:14:32.660405:
2025-01-20 20:14:32.694895: Epoch 348
2025-01-20 20:14:32.694959: Current learning rate: 0.0068
2025-01-20 20:15:20.381318: train_loss -0.7277
2025-01-20 20:15:20.381432: val_loss -0.705
2025-01-20 20:15:20.381505: Pseudo dice [np.float32(0.7618), np.float32(0.7738), np.float32(0.8574), np.float32(0.7643), np.float32(0.8937), np.float32(0.7824)]
2025-01-20 20:15:20.381546: Epoch time: 47.72 s
2025-01-20 20:15:20.853727:
2025-01-20 20:15:20.888152: Epoch 349
2025-01-20 20:15:20.888246: Current learning rate: 0.0068
2025-01-20 20:16:08.609316: train_loss -0.7207
2025-01-20 20:16:08.644441: val_loss -0.7193