Datasets:
ngaggion
/
ChronoRoot_nnUNet

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1.16k
2025-01-20 18:33:54.267103: Epoch time: 47.76 s
2025-01-20 18:33:54.737024:
2025-01-20 18:33:54.771496: Epoch 223
2025-01-20 18:33:54.771556: Current learning rate: 0.00797
2025-01-20 18:34:42.489485: train_loss -0.7139
2025-01-20 18:34:42.524578: val_loss -0.7092
2025-01-20 18:34:42.524645: Pseudo dice [np.float32(0.7549), np.float32(0.757), np.float32(0.8609), np.float32(0.7468), np.float32(0.8976), np.float32(0.7828)]
2025-01-20 18:34:42.524683: Epoch time: 47.75 s
2025-01-20 18:34:43.099683:
2025-01-20 18:34:43.134195: Epoch 224
2025-01-20 18:34:43.134274: Current learning rate: 0.00796
2025-01-20 18:35:30.885796: train_loss -0.7105
2025-01-20 18:35:30.920902: val_loss -0.6995
2025-01-20 18:35:30.920959: Pseudo dice [np.float32(0.7517), np.float32(0.7271), np.float32(0.8564), np.float32(0.77), np.float32(0.8834), np.float32(0.7694)]
2025-01-20 18:35:30.920997: Epoch time: 47.79 s
2025-01-20 18:35:31.386629:
2025-01-20 18:35:31.421001: Epoch 225
2025-01-20 18:35:31.421085: Current learning rate: 0.00795
2025-01-20 18:36:19.191121: train_loss -0.7043
2025-01-20 18:36:19.226239: val_loss -0.6977
2025-01-20 18:36:19.226295: Pseudo dice [np.float32(0.7593), np.float32(0.7756), np.float32(0.8637), np.float32(0.7737), np.float32(0.891), np.float32(0.7899)]
2025-01-20 18:36:19.226357: Epoch time: 47.81 s
2025-01-20 18:36:19.226378: Yayy! New best EMA pseudo Dice: 0.7993000149726868
2025-01-20 18:36:20.067530:
2025-01-20 18:36:20.067724: Epoch 226
2025-01-20 18:36:20.067797: Current learning rate: 0.00794
2025-01-20 18:37:07.809278: train_loss -0.711
2025-01-20 18:37:07.844386: val_loss -0.7107
2025-01-20 18:37:07.844467: Pseudo dice [np.float32(0.759), np.float32(0.7637), np.float32(0.8656), np.float32(0.7318), np.float32(0.8991), np.float32(0.7802)]
2025-01-20 18:37:07.844506: Epoch time: 47.74 s
2025-01-20 18:37:07.844532: Yayy! New best EMA pseudo Dice: 0.7993999719619751
2025-01-20 18:37:08.680856:
2025-01-20 18:37:08.683313: Epoch 227
2025-01-20 18:37:08.683414: Current learning rate: 0.00793
2025-01-20 18:37:56.452167: train_loss -0.7259
2025-01-20 18:37:56.487316: val_loss -0.7179
2025-01-20 18:37:56.487382: Pseudo dice [np.float32(0.7684), np.float32(0.7736), np.float32(0.8655), np.float32(0.7645), np.float32(0.9023), np.float32(0.7762)]
2025-01-20 18:37:56.487418: Epoch time: 47.77 s
2025-01-20 18:37:56.487438: Yayy! New best EMA pseudo Dice: 0.8003000020980835
2025-01-20 18:37:57.333433:
2025-01-20 18:37:57.368739: Epoch 228
2025-01-20 18:37:57.368829: Current learning rate: 0.00792
2025-01-20 18:38:45.095801: train_loss -0.7106
2025-01-20 18:38:45.130870: val_loss -0.6813
2025-01-20 18:38:45.130929: Pseudo dice [np.float32(0.7548), np.float32(0.7686), np.float32(0.8593), np.float32(0.7081), np.float32(0.8873), np.float32(0.7605)]
2025-01-20 18:38:45.130966: Epoch time: 47.76 s
2025-01-20 18:38:45.588333:
2025-01-20 18:38:45.622779: Epoch 229
2025-01-20 18:38:45.622844: Current learning rate: 0.00791
2025-01-20 18:39:33.366550: train_loss -0.7045
2025-01-20 18:39:33.401657: val_loss -0.6854
2025-01-20 18:39:33.401722: Pseudo dice [np.float32(0.7444), np.float32(0.7642), np.float32(0.8524), np.float32(0.7326), np.float32(0.8833), np.float32(0.739)]
2025-01-20 18:39:33.401782: Epoch time: 47.78 s
2025-01-20 18:39:33.856119:
2025-01-20 18:39:33.890589: Epoch 230
2025-01-20 18:39:33.890650: Current learning rate: 0.0079
2025-01-20 18:40:21.650016: train_loss -0.699
2025-01-20 18:40:21.685131: val_loss -0.7017
2025-01-20 18:40:21.685186: Pseudo dice [np.float32(0.7529), np.float32(0.7608), np.float32(0.8561), np.float32(0.7612), np.float32(0.885), np.float32(0.7911)]
2025-01-20 18:40:21.685236: Epoch time: 47.79 s
2025-01-20 18:40:22.140140:
2025-01-20 18:40:22.174613: Epoch 231
2025-01-20 18:40:22.174710: Current learning rate: 0.00789
2025-01-20 18:41:09.879554: train_loss -0.7028
2025-01-20 18:41:09.914692: val_loss -0.6635
2025-01-20 18:41:09.914787: Pseudo dice [np.float32(0.6702), np.float32(0.748), np.float32(0.8435), np.float32(0.7253), np.float32(0.8683), np.float32(0.783)]
2025-01-20 18:41:09.914837: Epoch time: 47.74 s
2025-01-20 18:41:10.370549:
2025-01-20 18:41:10.405000: Epoch 232
2025-01-20 18:41:10.405062: Current learning rate: 0.00789
2025-01-20 18:41:58.162215: train_loss -0.6997
2025-01-20 18:41:58.197292: val_loss -0.707
2025-01-20 18:41:58.197348: Pseudo dice [np.float32(0.7557), np.float32(0.7581), np.float32(0.8618), np.float32(0.7327), np.float32(0.8994), np.float32(0.7789)]
2025-01-20 18:41:58.197386: Epoch time: 47.79 s
2025-01-20 18:41:58.769770:
2025-01-20 18:41:58.804250: Epoch 233
2025-01-20 18:41:58.804347: Current learning rate: 0.00788
2025-01-20 18:42:46.621224: train_loss -0.7085
2025-01-20 18:42:46.656344: val_loss -0.7034
2025-01-20 18:42:46.656410: Pseudo dice [np.float32(0.7504), np.float32(0.7227), np.float32(0.8616), np.float32(0.7481), np.float32(0.883), np.float32(0.7595)]
2025-01-20 18:42:46.656472: Epoch time: 47.85 s
2025-01-20 18:42:47.111151:
2025-01-20 18:42:47.145611: Epoch 234
2025-01-20 18:42:47.145671: Current learning rate: 0.00787
2025-01-20 18:43:34.911249: train_loss -0.7074
2025-01-20 18:43:34.911429: val_loss -0.7072
2025-01-20 18:43:34.911492: Pseudo dice [np.float32(0.753), np.float32(0.763), np.float32(0.8636), np.float32(0.7174), np.float32(0.8896), np.float32(0.7658)]
2025-01-20 18:43:34.911552: Epoch time: 47.8 s
2025-01-20 18:43:35.366599:
2025-01-20 18:43:35.401087: Epoch 235
2025-01-20 18:43:35.401174: Current learning rate: 0.00786
2025-01-20 18:44:23.161379: train_loss -0.7123
2025-01-20 18:44:23.196480: val_loss -0.6917
2025-01-20 18:44:23.196562: Pseudo dice [np.float32(0.7659), np.float32(0.7441), np.float32(0.8497), np.float32(0.7594), np.float32(0.8848), np.float32(0.7716)]
2025-01-20 18:44:23.196606: Epoch time: 47.8 s
2025-01-20 18:44:23.652764:
2025-01-20 18:44:23.687201: Epoch 236
2025-01-20 18:44:23.687287: Current learning rate: 0.00785
2025-01-20 18:45:11.445948: train_loss -0.7113
2025-01-20 18:45:11.480999: val_loss -0.7067