Datasets:
ngaggion
/
ChronoRoot_nnUNet

Dataset card Data Studio Files Community
1
text
stringlengths
0
1.16k
2025-01-20 18:56:28.414684:
2025-01-20 18:56:28.449139: Epoch 251
2025-01-20 18:56:28.449225: Current learning rate: 0.00771
2025-01-20 18:57:16.210356: train_loss -0.7096
2025-01-20 18:57:16.245517: val_loss -0.7138
2025-01-20 18:57:16.245583: Pseudo dice [np.float32(0.7619), np.float32(0.7623), np.float32(0.8601), np.float32(0.7486), np.float32(0.8884), np.float32(0.7757)]
2025-01-20 18:57:16.245649: Epoch time: 47.8 s
2025-01-20 18:57:16.709011:
2025-01-20 18:57:16.743533: Epoch 252
2025-01-20 18:57:16.743613: Current learning rate: 0.0077
2025-01-20 18:58:04.475436: train_loss -0.7164
2025-01-20 18:58:04.510501: val_loss -0.6964
2025-01-20 18:58:04.510587: Pseudo dice [np.float32(0.7521), np.float32(0.7726), np.float32(0.8561), np.float32(0.7319), np.float32(0.8814), np.float32(0.7527)]
2025-01-20 18:58:04.510665: Epoch time: 47.77 s
2025-01-20 18:58:04.970821:
2025-01-20 18:58:05.005239: Epoch 253
2025-01-20 18:58:05.005349: Current learning rate: 0.00769
2025-01-20 18:58:52.738559: train_loss -0.7173
2025-01-20 18:58:52.773563: val_loss -0.7075
2025-01-20 18:58:52.773626: Pseudo dice [np.float32(0.7724), np.float32(0.7722), np.float32(0.8572), np.float32(0.7131), np.float32(0.8809), np.float32(0.7707)]
2025-01-20 18:58:52.773663: Epoch time: 47.77 s
2025-01-20 18:58:53.232494:
2025-01-20 18:58:53.266876: Epoch 254
2025-01-20 18:58:53.266986: Current learning rate: 0.00768
2025-01-20 18:59:41.018553: train_loss -0.7118
2025-01-20 18:59:41.053670: val_loss -0.7067
2025-01-20 18:59:41.053731: Pseudo dice [np.float32(0.764), np.float32(0.7716), np.float32(0.8539), np.float32(0.7494), np.float32(0.8951), np.float32(0.7948)]
2025-01-20 18:59:41.053769: Epoch time: 47.79 s
2025-01-20 18:59:41.514324:
2025-01-20 18:59:41.548835: Epoch 255
2025-01-20 18:59:41.548900: Current learning rate: 0.00767
2025-01-20 19:00:29.319104: train_loss -0.7156
2025-01-20 19:00:29.354284: val_loss -0.7025
2025-01-20 19:00:29.354340: Pseudo dice [np.float32(0.751), np.float32(0.7365), np.float32(0.8543), np.float32(0.7321), np.float32(0.8958), np.float32(0.7837)]
2025-01-20 19:00:29.354384: Epoch time: 47.81 s
2025-01-20 19:00:29.813326:
2025-01-20 19:00:29.847872: Epoch 256
2025-01-20 19:00:29.847959: Current learning rate: 0.00766
2025-01-20 19:01:17.552877: train_loss -0.7233
2025-01-20 19:01:17.587993: val_loss -0.7047
2025-01-20 19:01:17.588047: Pseudo dice [np.float32(0.7533), np.float32(0.7698), np.float32(0.8612), np.float32(0.7383), np.float32(0.8975), np.float32(0.7711)]
2025-01-20 19:01:17.588082: Epoch time: 47.74 s
2025-01-20 19:01:18.046633:
2025-01-20 19:01:18.081141: Epoch 257
2025-01-20 19:01:18.081215: Current learning rate: 0.00765
2025-01-20 19:02:05.805580: train_loss -0.717
2025-01-20 19:02:05.840739: val_loss -0.7277
2025-01-20 19:02:05.840803: Pseudo dice [np.float32(0.7663), np.float32(0.7723), np.float32(0.8626), np.float32(0.7608), np.float32(0.8932), np.float32(0.7965)]
2025-01-20 19:02:05.840863: Epoch time: 47.76 s
2025-01-20 19:02:06.411391:
2025-01-20 19:02:06.445860: Epoch 258
2025-01-20 19:02:06.445939: Current learning rate: 0.00764
2025-01-20 19:02:54.253989: train_loss -0.7069
2025-01-20 19:02:54.289136: val_loss -0.6931
2025-01-20 19:02:54.289189: Pseudo dice [np.float32(0.747), np.float32(0.7489), np.float32(0.8617), np.float32(0.7432), np.float32(0.8901), np.float32(0.8007)]
2025-01-20 19:02:54.289226: Epoch time: 47.84 s
2025-01-20 19:02:54.750633:
2025-01-20 19:02:54.785003: Epoch 259
2025-01-20 19:02:54.785096: Current learning rate: 0.00764
2025-01-20 19:03:42.517266: train_loss -0.7043
2025-01-20 19:03:42.552640: val_loss -0.7108
2025-01-20 19:03:42.552713: Pseudo dice [np.float32(0.7641), np.float32(0.7834), np.float32(0.8605), np.float32(0.7375), np.float32(0.8877), np.float32(0.7795)]
2025-01-20 19:03:42.552753: Epoch time: 47.77 s
2025-01-20 19:03:43.015034:
2025-01-20 19:03:43.050741: Epoch 260
2025-01-20 19:03:43.050835: Current learning rate: 0.00763
2025-01-20 19:04:30.779308: train_loss -0.7067
2025-01-20 19:04:30.814390: val_loss -0.693
2025-01-20 19:04:30.814469: Pseudo dice [np.float32(0.7629), np.float32(0.7721), np.float32(0.8566), np.float32(0.7539), np.float32(0.8881), np.float32(0.7682)]
2025-01-20 19:04:30.814506: Epoch time: 47.76 s
2025-01-20 19:04:31.274900:
2025-01-20 19:04:31.309368: Epoch 261
2025-01-20 19:04:31.309430: Current learning rate: 0.00762
2025-01-20 19:05:19.029166: train_loss -0.7092
2025-01-20 19:05:19.064291: val_loss -0.6962
2025-01-20 19:05:19.064345: Pseudo dice [np.float32(0.7462), np.float32(0.7173), np.float32(0.861), np.float32(0.7421), np.float32(0.8963), np.float32(0.7802)]
2025-01-20 19:05:19.064398: Epoch time: 47.75 s
2025-01-20 19:05:19.528101:
2025-01-20 19:05:19.562601: Epoch 262
2025-01-20 19:05:19.562665: Current learning rate: 0.00761
2025-01-20 19:06:07.318375: train_loss -0.7199
2025-01-20 19:06:07.353523: val_loss -0.7141
2025-01-20 19:06:07.353589: Pseudo dice [np.float32(0.7447), np.float32(0.7805), np.float32(0.8553), np.float32(0.7436), np.float32(0.8912), np.float32(0.7914)]
2025-01-20 19:06:07.353646: Epoch time: 47.79 s
2025-01-20 19:06:07.814999:
2025-01-20 19:06:07.849508: Epoch 263
2025-01-20 19:06:07.849586: Current learning rate: 0.0076
2025-01-20 19:06:55.561364: train_loss -0.7089
2025-01-20 19:06:55.596536: val_loss -0.7153
2025-01-20 19:06:55.596608: Pseudo dice [np.float32(0.7584), np.float32(0.7715), np.float32(0.8559), np.float32(0.7063), np.float32(0.8965), np.float32(0.7797)]
2025-01-20 19:06:55.596644: Epoch time: 47.75 s
2025-01-20 19:06:56.058960:
2025-01-20 19:06:56.093391: Epoch 264
2025-01-20 19:06:56.093453: Current learning rate: 0.00759
2025-01-20 19:07:43.799966: train_loss -0.7073
2025-01-20 19:07:43.835082: val_loss -0.7087
2025-01-20 19:07:43.835136: Pseudo dice [np.float32(0.7559), np.float32(0.7733), np.float32(0.8585), np.float32(0.7005), np.float32(0.9018), np.float32(0.774)]
2025-01-20 19:07:43.835178: Epoch time: 47.74 s
2025-01-20 19:07:44.296361:
2025-01-20 19:07:44.330864: Epoch 265