Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 19:30:18.407477: Pseudo dice [np.float32(0.7721), np.float32(0.7717), np.float32(0.8681), np.float32(0.7666), np.float32(0.8719), np.float32(0.7649)]
2025-01-20 19:30:18.407521: Epoch time: 47.71 s
2025-01-20 19:30:18.871507:
2025-01-20 19:30:18.906066: Epoch 293
2025-01-20 19:30:18.906145: Current learning rate: 0.00732
2025-01-20 19:31:06.621758: train_loss -0.7265
2025-01-20 19:31:06.656900: val_loss -0.6966
2025-01-20 19:31:06.656975: Pseudo dice [np.float32(0.7575), np.float32(0.7933), np.float32(0.8655), np.float32(0.7608), np.float32(0.8837), np.float32(0.7757)]
2025-01-20 19:31:06.657018: Epoch time: 47.75 s
2025-01-20 19:31:07.122281:
2025-01-20 19:31:07.156791: Epoch 294
2025-01-20 19:31:07.156854: Current learning rate: 0.00731
2025-01-20 19:31:54.870720: train_loss -0.7184
2025-01-20 19:31:54.870907: val_loss -0.7043
2025-01-20 19:31:54.870958: Pseudo dice [np.float32(0.761), np.float32(0.7543), np.float32(0.8587), np.float32(0.7633), np.float32(0.8544), np.float32(0.7784)]
2025-01-20 19:31:54.870994: Epoch time: 47.75 s
2025-01-20 19:31:55.336284:
2025-01-20 19:31:55.370791: Epoch 295
2025-01-20 19:31:55.370863: Current learning rate: 0.0073
2025-01-20 19:32:43.087709: train_loss -0.7121
2025-01-20 19:32:43.122831: val_loss -0.7083
2025-01-20 19:32:43.122886: Pseudo dice [np.float32(0.7599), np.float32(0.7579), np.float32(0.8551), np.float32(0.7426), np.float32(0.8843), np.float32(0.7753)]
2025-01-20 19:32:43.122923: Epoch time: 47.75 s
2025-01-20 19:32:43.587944:
2025-01-20 19:32:43.622429: Epoch 296
2025-01-20 19:32:43.622491: Current learning rate: 0.00729
2025-01-20 19:33:31.344806: train_loss -0.7172
2025-01-20 19:33:31.379895: val_loss -0.7226
2025-01-20 19:33:31.379949: Pseudo dice [np.float32(0.752), np.float32(0.7675), np.float32(0.8556), np.float32(0.7543), np.float32(0.8952), np.float32(0.7804)]
2025-01-20 19:33:31.379989: Epoch time: 47.76 s
2025-01-20 19:33:31.845527:
2025-01-20 19:33:31.879852: Epoch 297
2025-01-20 19:33:31.879933: Current learning rate: 0.00728
2025-01-20 19:34:19.562454: train_loss -0.7237
2025-01-20 19:34:19.597599: val_loss -0.6845
2025-01-20 19:34:19.597654: Pseudo dice [np.float32(0.7509), np.float32(0.7511), np.float32(0.8631), np.float32(0.7489), np.float32(0.8767), np.float32(0.7707)]
2025-01-20 19:34:19.597691: Epoch time: 47.72 s
2025-01-20 19:34:20.064323:
2025-01-20 19:34:20.098678: Epoch 298
2025-01-20 19:34:20.098814: Current learning rate: 0.00727
2025-01-20 19:35:07.828319: train_loss -0.7224
2025-01-20 19:35:07.863415: val_loss -0.7
2025-01-20 19:35:07.863469: Pseudo dice [np.float32(0.7628), np.float32(0.7897), np.float32(0.8584), np.float32(0.7305), np.float32(0.875), np.float32(0.7828)]
2025-01-20 19:35:07.863509: Epoch time: 47.76 s
2025-01-20 19:35:08.438869:
2025-01-20 19:35:08.473329: Epoch 299
2025-01-20 19:35:08.473408: Current learning rate: 0.00726
2025-01-20 19:35:56.224233: train_loss -0.7211
2025-01-20 19:35:56.259275: val_loss -0.7196
2025-01-20 19:35:56.259330: Pseudo dice [np.float32(0.772), np.float32(0.7797), np.float32(0.866), np.float32(0.7409), np.float32(0.8891), np.float32(0.7909)]
2025-01-20 19:35:56.259366: Epoch time: 47.79 s
2025-01-20 19:35:57.111987:
2025-01-20 19:35:57.117608: Epoch 300
2025-01-20 19:35:57.117696: Current learning rate: 0.00725
2025-01-20 19:36:44.817273: train_loss -0.7251
2025-01-20 19:36:44.852317: val_loss -0.7214
2025-01-20 19:36:44.852375: Pseudo dice [np.float32(0.7738), np.float32(0.7782), np.float32(0.87), np.float32(0.7728), np.float32(0.8961), np.float32(0.7765)]
2025-01-20 19:36:44.852417: Epoch time: 47.71 s
2025-01-20 19:36:45.315907:
2025-01-20 19:36:45.350341: Epoch 301
2025-01-20 19:36:45.350430: Current learning rate: 0.00724
2025-01-20 19:37:33.089324: train_loss -0.7224
2025-01-20 19:37:33.124311: val_loss -0.6951
2025-01-20 19:37:33.124613: Pseudo dice [np.float32(0.766), np.float32(0.7709), np.float32(0.8566), np.float32(0.7408), np.float32(0.8686), np.float32(0.7794)]
2025-01-20 19:37:33.124665: Epoch time: 47.77 s
2025-01-20 19:37:33.589479:
2025-01-20 19:37:33.623976: Epoch 302
2025-01-20 19:37:33.624061: Current learning rate: 0.00724
2025-01-20 19:38:21.379228: train_loss -0.7263
2025-01-20 19:38:21.414328: val_loss -0.7239
2025-01-20 19:38:21.414383: Pseudo dice [np.float32(0.767), np.float32(0.7951), np.float32(0.8625), np.float32(0.7698), np.float32(0.9002), np.float32(0.7968)]
2025-01-20 19:38:21.414420: Epoch time: 47.79 s
2025-01-20 19:38:21.879298:
2025-01-20 19:38:21.913777: Epoch 303
2025-01-20 19:38:21.913882: Current learning rate: 0.00723
2025-01-20 19:39:09.589595: train_loss -0.7212
2025-01-20 19:39:09.624679: val_loss -0.7037
2025-01-20 19:39:09.624751: Pseudo dice [np.float32(0.7672), np.float32(0.7806), np.float32(0.8657), np.float32(0.7625), np.float32(0.9024), np.float32(0.8031)]
2025-01-20 19:39:09.624802: Epoch time: 47.71 s
2025-01-20 19:39:09.624824: Yayy! New best EMA pseudo Dice: 0.803600013256073
2025-01-20 19:39:10.486338:
2025-01-20 19:39:10.489276: Epoch 304
2025-01-20 19:39:10.489356: Current learning rate: 0.00722
2025-01-20 19:39:58.188580: train_loss -0.714
2025-01-20 19:39:58.223734: val_loss -0.7079
2025-01-20 19:39:58.223789: Pseudo dice [np.float32(0.755), np.float32(0.7654), np.float32(0.8505), np.float32(0.7324), np.float32(0.8732), np.float32(0.7704)]
2025-01-20 19:39:58.223827: Epoch time: 47.7 s
2025-01-20 19:39:58.691678:
2025-01-20 19:39:58.726169: Epoch 305
2025-01-20 19:39:58.726234: Current learning rate: 0.00721
2025-01-20 19:40:46.437459: train_loss -0.7226
2025-01-20 19:40:46.472452: val_loss -0.7197
2025-01-20 19:40:46.472514: Pseudo dice [np.float32(0.7748), np.float32(0.7681), np.float32(0.8598), np.float32(0.7732), np.float32(0.8985), np.float32(0.7572)]
2025-01-20 19:40:46.472558: Epoch time: 47.75 s
2025-01-20 19:40:46.937059:
2025-01-20 19:40:46.971506: Epoch 306
2025-01-20 19:40:46.971568: Current learning rate: 0.0072
2025-01-20 19:41:34.632276: train_loss -0.7289
2025-01-20 19:41:34.667238: val_loss -0.7079
2025-01-20 19:41:34.667315: Pseudo dice [np.float32(0.7681), np.float32(0.7783), np.float32(0.8657), np.float32(0.7415), np.float32(0.8877), np.float32(0.782)]