Datasets:
ngaggion
/
ChronoRoot_nnUNet

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
2025-01-20 21:00:26.407106: Current learning rate: 0.00627
2025-01-20 21:01:14.139461: train_loss -0.7364
2025-01-20 21:01:14.174514: val_loss -0.7182
2025-01-20 21:01:14.174606: Pseudo dice [np.float32(0.7603), np.float32(0.7783), np.float32(0.8627), np.float32(0.7763), np.float32(0.8847), np.float32(0.7838)]
2025-01-20 21:01:14.174650: Epoch time: 47.77 s
2025-01-20 21:01:14.646317:
2025-01-20 21:01:14.680804: Epoch 406
2025-01-20 21:01:14.680894: Current learning rate: 0.00626
2025-01-20 21:02:02.432526: train_loss -0.7295
2025-01-20 21:02:02.467629: val_loss -0.7245
2025-01-20 21:02:02.467685: Pseudo dice [np.float32(0.7726), np.float32(0.7792), np.float32(0.8669), np.float32(0.7644), np.float32(0.8849), np.float32(0.7683)]
2025-01-20 21:02:02.467727: Epoch time: 47.79 s
2025-01-20 21:02:02.938215:
2025-01-20 21:02:02.972723: Epoch 407
2025-01-20 21:02:02.972801: Current learning rate: 0.00625
2025-01-20 21:02:50.715997: train_loss -0.7332
2025-01-20 21:02:50.751043: val_loss -0.7277
2025-01-20 21:02:50.751111: Pseudo dice [np.float32(0.7673), np.float32(0.7788), np.float32(0.8678), np.float32(0.7591), np.float32(0.9032), np.float32(0.7873)]
2025-01-20 21:02:50.751146: Epoch time: 47.78 s
2025-01-20 21:02:51.223050:
2025-01-20 21:02:51.257602: Epoch 408
2025-01-20 21:02:51.257678: Current learning rate: 0.00624
2025-01-20 21:03:38.934134: train_loss -0.7344
2025-01-20 21:03:38.969232: val_loss -0.7297
2025-01-20 21:03:38.969298: Pseudo dice [np.float32(0.772), np.float32(0.7645), np.float32(0.8635), np.float32(0.7558), np.float32(0.9017), np.float32(0.7903)]
2025-01-20 21:03:38.969334: Epoch time: 47.71 s
2025-01-20 21:03:39.554013:
2025-01-20 21:03:39.588482: Epoch 409
2025-01-20 21:03:39.588584: Current learning rate: 0.00623
2025-01-20 21:04:27.315846: train_loss -0.7314
2025-01-20 21:04:27.350951: val_loss -0.7247
2025-01-20 21:04:27.351005: Pseudo dice [np.float32(0.7644), np.float32(0.7952), np.float32(0.8642), np.float32(0.7417), np.float32(0.8961), np.float32(0.8038)]
2025-01-20 21:04:27.351055: Epoch time: 47.76 s
2025-01-20 21:04:27.351079: Yayy! New best EMA pseudo Dice: 0.8077999949455261
2025-01-20 21:04:28.214123:
2025-01-20 21:04:28.249471: Epoch 410
2025-01-20 21:04:28.249547: Current learning rate: 0.00622
2025-01-20 21:05:15.977433: train_loss -0.7256
2025-01-20 21:05:16.012661: val_loss -0.7348
2025-01-20 21:05:16.012757: Pseudo dice [np.float32(0.7817), np.float32(0.7825), np.float32(0.8601), np.float32(0.751), np.float32(0.903), np.float32(0.7953)]
2025-01-20 21:05:16.012826: Epoch time: 47.76 s
2025-01-20 21:05:16.012847: Yayy! New best EMA pseudo Dice: 0.8082000017166138
2025-01-20 21:05:16.858103:
2025-01-20 21:05:16.859736: Epoch 411
2025-01-20 21:05:16.859810: Current learning rate: 0.00621
2025-01-20 21:06:04.589853: train_loss -0.7204
2025-01-20 21:06:04.625032: val_loss -0.7347
2025-01-20 21:06:04.625122: Pseudo dice [np.float32(0.7817), np.float32(0.7928), np.float32(0.8719), np.float32(0.7599), np.float32(0.8954), np.float32(0.7928)]
2025-01-20 21:06:04.625165: Epoch time: 47.73 s
2025-01-20 21:06:04.625191: Yayy! New best EMA pseudo Dice: 0.8090000152587891
2025-01-20 21:06:05.470578:
2025-01-20 21:06:05.505917: Epoch 412
2025-01-20 21:06:05.506015: Current learning rate: 0.0062
2025-01-20 21:06:53.222768: train_loss -0.7186
2025-01-20 21:06:53.257894: val_loss -0.7217
2025-01-20 21:06:53.257974: Pseudo dice [np.float32(0.7696), np.float32(0.7781), np.float32(0.8623), np.float32(0.7722), np.float32(0.8976), np.float32(0.798)]
2025-01-20 21:06:53.258011: Epoch time: 47.75 s
2025-01-20 21:06:53.258032: Yayy! New best EMA pseudo Dice: 0.8094000220298767
2025-01-20 21:06:54.098011:
2025-01-20 21:06:54.102359: Epoch 413
2025-01-20 21:06:54.102443: Current learning rate: 0.00619
2025-01-20 21:07:41.818399: train_loss -0.7322
2025-01-20 21:07:41.853623: val_loss -0.7345
2025-01-20 21:07:41.853703: Pseudo dice [np.float32(0.7722), np.float32(0.7842), np.float32(0.8595), np.float32(0.7702), np.float32(0.8977), np.float32(0.7924)]
2025-01-20 21:07:41.853758: Epoch time: 47.72 s
2025-01-20 21:07:41.853781: Yayy! New best EMA pseudo Dice: 0.8097000122070312
2025-01-20 21:07:42.710472:
2025-01-20 21:07:42.745860: Epoch 414
2025-01-20 21:07:42.745944: Current learning rate: 0.00618
2025-01-20 21:08:30.469982: train_loss -0.7302
2025-01-20 21:08:30.505090: val_loss -0.7372
2025-01-20 21:08:30.505144: Pseudo dice [np.float32(0.7716), np.float32(0.7775), np.float32(0.8655), np.float32(0.7489), np.float32(0.883), np.float32(0.7696)]
2025-01-20 21:08:30.505181: Epoch time: 47.76 s
2025-01-20 21:08:30.963919:
2025-01-20 21:08:30.998310: Epoch 415
2025-01-20 21:08:30.998399: Current learning rate: 0.00617
2025-01-20 21:09:18.733773: train_loss -0.7308
2025-01-20 21:09:18.768900: val_loss -0.7276
2025-01-20 21:09:18.768972: Pseudo dice [np.float32(0.7699), np.float32(0.7974), np.float32(0.872), np.float32(0.7422), np.float32(0.9059), np.float32(0.8011)]
2025-01-20 21:09:18.769033: Epoch time: 47.77 s
2025-01-20 21:09:19.225823:
2025-01-20 21:09:19.260304: Epoch 416
2025-01-20 21:09:19.260378: Current learning rate: 0.00616
2025-01-20 21:10:06.987636: train_loss -0.7218
2025-01-20 21:10:07.022754: val_loss -0.7335
2025-01-20 21:10:07.022813: Pseudo dice [np.float32(0.7705), np.float32(0.7809), np.float32(0.8664), np.float32(0.7638), np.float32(0.896), np.float32(0.809)]
2025-01-20 21:10:07.022850: Epoch time: 47.76 s
2025-01-20 21:10:07.022871: Yayy! New best EMA pseudo Dice: 0.8101000189781189
2025-01-20 21:10:07.984074:
2025-01-20 21:10:07.986760: Epoch 417
2025-01-20 21:10:07.986857: Current learning rate: 0.00615
2025-01-20 21:10:55.730850: train_loss -0.7413
2025-01-20 21:10:55.766020: val_loss -0.7209
2025-01-20 21:10:55.766073: Pseudo dice [np.float32(0.7733), np.float32(0.7984), np.float32(0.8657), np.float32(0.7697), np.float32(0.9026), np.float32(0.7989)]
2025-01-20 21:10:55.766112: Epoch time: 47.75 s
2025-01-20 21:10:55.766145: Yayy! New best EMA pseudo Dice: 0.8108999729156494
2025-01-20 21:10:56.613457:
2025-01-20 21:10:56.648840: Epoch 418
2025-01-20 21:10:56.648923: Current learning rate: 0.00614
2025-01-20 21:11:44.370262: train_loss -0.7187