Datasets:
ngaggion
/
ChronoRoot_nnUNet

Dataset card Data Studio Files Community
1
text
stringlengths
0
1.16k
2025-01-20 19:41:34.667352: Epoch time: 47.7 s
2025-01-20 19:41:35.243466:
2025-01-20 19:41:35.277891: Epoch 307
2025-01-20 19:41:35.277980: Current learning rate: 0.00719
2025-01-20 19:42:23.048558: train_loss -0.7128
2025-01-20 19:42:23.083674: val_loss -0.7105
2025-01-20 19:42:23.083778: Pseudo dice [np.float32(0.7579), np.float32(0.7645), np.float32(0.8641), np.float32(0.7589), np.float32(0.8996), np.float32(0.7844)]
2025-01-20 19:42:23.083828: Epoch time: 47.81 s
2025-01-20 19:42:23.548234:
2025-01-20 19:42:23.582682: Epoch 308
2025-01-20 19:42:23.582775: Current learning rate: 0.00718
2025-01-20 19:43:11.320117: train_loss -0.7132
2025-01-20 19:43:11.355232: val_loss -0.7184
2025-01-20 19:43:11.355286: Pseudo dice [np.float32(0.7578), np.float32(0.7602), np.float32(0.8579), np.float32(0.787), np.float32(0.8949), np.float32(0.7774)]
2025-01-20 19:43:11.355322: Epoch time: 47.77 s
2025-01-20 19:43:11.819617:
2025-01-20 19:43:11.854104: Epoch 309
2025-01-20 19:43:11.854172: Current learning rate: 0.00717
2025-01-20 19:43:59.588244: train_loss -0.7182
2025-01-20 19:43:59.623343: val_loss -0.709
2025-01-20 19:43:59.623399: Pseudo dice [np.float32(0.7531), np.float32(0.7759), np.float32(0.8555), np.float32(0.7357), np.float32(0.8991), np.float32(0.7921)]
2025-01-20 19:43:59.623462: Epoch time: 47.77 s
2025-01-20 19:44:00.087831:
2025-01-20 19:44:00.122285: Epoch 310
2025-01-20 19:44:00.122398: Current learning rate: 0.00716
2025-01-20 19:44:47.807651: train_loss -0.7231
2025-01-20 19:44:47.842768: val_loss -0.7148
2025-01-20 19:44:47.842844: Pseudo dice [np.float32(0.7677), np.float32(0.7743), np.float32(0.8638), np.float32(0.7422), np.float32(0.8969), np.float32(0.7879)]
2025-01-20 19:44:47.842889: Epoch time: 47.72 s
2025-01-20 19:44:48.306118:
2025-01-20 19:44:48.340571: Epoch 311
2025-01-20 19:44:48.340637: Current learning rate: 0.00715
2025-01-20 19:45:36.008353: train_loss -0.7176
2025-01-20 19:45:36.043461: val_loss -0.7129
2025-01-20 19:45:36.043515: Pseudo dice [np.float32(0.7636), np.float32(0.7319), np.float32(0.8595), np.float32(0.7286), np.float32(0.8904), np.float32(0.7766)]
2025-01-20 19:45:36.043551: Epoch time: 47.7 s
2025-01-20 19:45:36.508201:
2025-01-20 19:45:36.542682: Epoch 312
2025-01-20 19:45:36.542783: Current learning rate: 0.00714
2025-01-20 19:46:24.218786: train_loss -0.7299
2025-01-20 19:46:24.253925: val_loss -0.7231
2025-01-20 19:46:24.253979: Pseudo dice [np.float32(0.7674), np.float32(0.783), np.float32(0.8678), np.float32(0.7798), np.float32(0.8958), np.float32(0.786)]
2025-01-20 19:46:24.254015: Epoch time: 47.71 s
2025-01-20 19:46:24.722276:
2025-01-20 19:46:24.756760: Epoch 313
2025-01-20 19:46:24.756850: Current learning rate: 0.00713
2025-01-20 19:47:12.478148: train_loss -0.7219
2025-01-20 19:47:12.513309: val_loss -0.7176
2025-01-20 19:47:12.513362: Pseudo dice [np.float32(0.7551), np.float32(0.7547), np.float32(0.8648), np.float32(0.7607), np.float32(0.8943), np.float32(0.7736)]
2025-01-20 19:47:12.513398: Epoch time: 47.76 s
2025-01-20 19:47:12.980363:
2025-01-20 19:47:13.014945: Epoch 314
2025-01-20 19:47:13.015038: Current learning rate: 0.00712
2025-01-20 19:48:00.685374: train_loss -0.7236
2025-01-20 19:48:00.720368: val_loss -0.7113
2025-01-20 19:48:00.720446: Pseudo dice [np.float32(0.7549), np.float32(0.7168), np.float32(0.858), np.float32(0.7583), np.float32(0.8947), np.float32(0.782)]
2025-01-20 19:48:00.720484: Epoch time: 47.71 s
2025-01-20 19:48:01.191078:
2025-01-20 19:48:01.225641: Epoch 315
2025-01-20 19:48:01.225709: Current learning rate: 0.00711
2025-01-20 19:48:48.912237: train_loss -0.7287
2025-01-20 19:48:48.947423: val_loss -0.7127
2025-01-20 19:48:48.947486: Pseudo dice [np.float32(0.7612), np.float32(0.7602), np.float32(0.8627), np.float32(0.7355), np.float32(0.8899), np.float32(0.7898)]
2025-01-20 19:48:48.947540: Epoch time: 47.72 s
2025-01-20 19:48:49.531275:
2025-01-20 19:48:49.565735: Epoch 316
2025-01-20 19:48:49.565825: Current learning rate: 0.0071
2025-01-20 19:49:37.283185: train_loss -0.7196
2025-01-20 19:49:37.318333: val_loss -0.7271
2025-01-20 19:49:37.318391: Pseudo dice [np.float32(0.7703), np.float32(0.7694), np.float32(0.8662), np.float32(0.7529), np.float32(0.8942), np.float32(0.7914)]
2025-01-20 19:49:37.318428: Epoch time: 47.75 s
2025-01-20 19:49:37.787181:
2025-01-20 19:49:37.821672: Epoch 317
2025-01-20 19:49:37.821739: Current learning rate: 0.0071
2025-01-20 19:50:25.549269: train_loss -0.7236
2025-01-20 19:50:25.584379: val_loss -0.7285
2025-01-20 19:50:25.584434: Pseudo dice [np.float32(0.7467), np.float32(0.7692), np.float32(0.8619), np.float32(0.7696), np.float32(0.897), np.float32(0.7766)]
2025-01-20 19:50:25.584471: Epoch time: 47.76 s
2025-01-20 19:50:26.056146:
2025-01-20 19:50:26.090638: Epoch 318
2025-01-20 19:50:26.090698: Current learning rate: 0.00709
2025-01-20 19:51:13.772724: train_loss -0.7156
2025-01-20 19:51:13.807863: val_loss -0.7083
2025-01-20 19:51:13.807918: Pseudo dice [np.float32(0.7525), np.float32(0.7672), np.float32(0.8557), np.float32(0.7479), np.float32(0.8832), np.float32(0.7681)]
2025-01-20 19:51:13.807954: Epoch time: 47.72 s
2025-01-20 19:51:14.274292:
2025-01-20 19:51:14.308758: Epoch 319
2025-01-20 19:51:14.308822: Current learning rate: 0.00708
2025-01-20 19:52:02.034441: train_loss -0.715
2025-01-20 19:52:02.069453: val_loss -0.6959
2025-01-20 19:52:02.069533: Pseudo dice [np.float32(0.7569), np.float32(0.7626), np.float32(0.8671), np.float32(0.7347), np.float32(0.8812), np.float32(0.7623)]
2025-01-20 19:52:02.069571: Epoch time: 47.76 s
2025-01-20 19:52:02.536604:
2025-01-20 19:52:02.571048: Epoch 320
2025-01-20 19:52:02.571133: Current learning rate: 0.00707
2025-01-20 19:52:50.287395: train_loss -0.717
2025-01-20 19:52:50.322501: val_loss -0.736
2025-01-20 19:52:50.322555: Pseudo dice [np.float32(0.7717), np.float32(0.7779), np.float32(0.8601), np.float32(0.7368), np.float32(0.8921), np.float32(0.7712)]
2025-01-20 19:52:50.322592: Epoch time: 47.75 s
2025-01-20 19:52:50.791816: