Datasets:
ngaggion
/
ChronoRoot_nnUNet

Dataset card Data Studio Files Community
1
text
stringlengths
0
1.16k
2025-01-20 18:22:37.642998:
2025-01-20 18:22:37.677370: Epoch 209
2025-01-20 18:22:37.677441: Current learning rate: 0.0081
2025-01-20 18:23:25.411681: train_loss -0.7096
2025-01-20 18:23:25.446794: val_loss -0.6967
2025-01-20 18:23:25.446849: Pseudo dice [np.float32(0.7622), np.float32(0.74), np.float32(0.8603), np.float32(0.7433), np.float32(0.8741), np.float32(0.7799)]
2025-01-20 18:23:25.446884: Epoch time: 47.77 s
2025-01-20 18:23:25.906246:
2025-01-20 18:23:25.940710: Epoch 210
2025-01-20 18:23:25.940773: Current learning rate: 0.00809
2025-01-20 18:24:13.674424: train_loss -0.6965
2025-01-20 18:24:13.709583: val_loss -0.6853
2025-01-20 18:24:13.709638: Pseudo dice [np.float32(0.7486), np.float32(0.7644), np.float32(0.853), np.float32(0.7274), np.float32(0.8917), np.float32(0.7708)]
2025-01-20 18:24:13.709677: Epoch time: 47.77 s
2025-01-20 18:24:14.171925:
2025-01-20 18:24:14.206378: Epoch 211
2025-01-20 18:24:14.206442: Current learning rate: 0.00808
2025-01-20 18:25:01.990164: train_loss -0.7087
2025-01-20 18:25:02.025224: val_loss -0.7038
2025-01-20 18:25:02.025287: Pseudo dice [np.float32(0.7434), np.float32(0.7534), np.float32(0.8633), np.float32(0.7514), np.float32(0.8918), np.float32(0.7846)]
2025-01-20 18:25:02.025325: Epoch time: 47.82 s
2025-01-20 18:25:02.484312:
2025-01-20 18:25:02.518817: Epoch 212
2025-01-20 18:25:02.518882: Current learning rate: 0.00807
2025-01-20 18:25:50.323160: train_loss -0.7191
2025-01-20 18:25:50.358227: val_loss -0.698
2025-01-20 18:25:50.358284: Pseudo dice [np.float32(0.7552), np.float32(0.7673), np.float32(0.8552), np.float32(0.7629), np.float32(0.8851), np.float32(0.7868)]
2025-01-20 18:25:50.358329: Epoch time: 47.84 s
2025-01-20 18:25:50.821056:
2025-01-20 18:25:50.855535: Epoch 213
2025-01-20 18:25:50.855622: Current learning rate: 0.00806
2025-01-20 18:26:38.593921: train_loss -0.7042
2025-01-20 18:26:38.628896: val_loss -0.6987
2025-01-20 18:26:38.628979: Pseudo dice [np.float32(0.7438), np.float32(0.7844), np.float32(0.8679), np.float32(0.729), np.float32(0.8806), np.float32(0.7577)]
2025-01-20 18:26:38.629023: Epoch time: 47.77 s
2025-01-20 18:26:39.089199:
2025-01-20 18:26:39.123521: Epoch 214
2025-01-20 18:26:39.123636: Current learning rate: 0.00805
2025-01-20 18:27:26.888928: train_loss -0.7078
2025-01-20 18:27:26.924080: val_loss -0.6975
2025-01-20 18:27:26.924162: Pseudo dice [np.float32(0.7498), np.float32(0.7448), np.float32(0.8506), np.float32(0.7396), np.float32(0.8787), np.float32(0.7665)]
2025-01-20 18:27:26.924203: Epoch time: 47.8 s
2025-01-20 18:27:27.386685:
2025-01-20 18:27:27.421212: Epoch 215
2025-01-20 18:27:27.421276: Current learning rate: 0.00804
2025-01-20 18:28:15.131093: train_loss -0.7054
2025-01-20 18:28:15.131261: val_loss -0.712
2025-01-20 18:28:15.131313: Pseudo dice [np.float32(0.7581), np.float32(0.7609), np.float32(0.8639), np.float32(0.7484), np.float32(0.8877), np.float32(0.793)]
2025-01-20 18:28:15.131350: Epoch time: 47.74 s
2025-01-20 18:28:15.706532:
2025-01-20 18:28:15.741066: Epoch 216
2025-01-20 18:28:15.741134: Current learning rate: 0.00803
2025-01-20 18:29:03.484548: train_loss -0.7145
2025-01-20 18:29:03.535970: val_loss -0.7117
2025-01-20 18:29:03.536060: Pseudo dice [np.float32(0.7589), np.float32(0.7774), np.float32(0.8661), np.float32(0.7522), np.float32(0.8921), np.float32(0.7914)]
2025-01-20 18:29:03.536101: Epoch time: 47.78 s
2025-01-20 18:29:03.997322:
2025-01-20 18:29:04.031768: Epoch 217
2025-01-20 18:29:04.031855: Current learning rate: 0.00802
2025-01-20 18:29:51.815516: train_loss -0.7141
2025-01-20 18:29:51.850465: val_loss -0.6829
2025-01-20 18:29:51.850531: Pseudo dice [np.float32(0.7558), np.float32(0.7799), np.float32(0.8488), np.float32(0.7565), np.float32(0.8693), np.float32(0.7778)]
2025-01-20 18:29:51.850566: Epoch time: 47.82 s
2025-01-20 18:29:52.311579:
2025-01-20 18:29:52.345995: Epoch 218
2025-01-20 18:29:52.346088: Current learning rate: 0.00801
2025-01-20 18:30:40.075619: train_loss -0.712
2025-01-20 18:30:40.110636: val_loss -0.72
2025-01-20 18:30:40.110730: Pseudo dice [np.float32(0.7715), np.float32(0.7936), np.float32(0.8681), np.float32(0.7694), np.float32(0.9003), np.float32(0.7917)]
2025-01-20 18:30:40.110787: Epoch time: 47.76 s
2025-01-20 18:30:40.110813: Yayy! New best EMA pseudo Dice: 0.7986000180244446
2025-01-20 18:30:40.960500:
2025-01-20 18:30:40.962853: Epoch 219
2025-01-20 18:30:40.962915: Current learning rate: 0.00801
2025-01-20 18:31:28.709599: train_loss -0.7104
2025-01-20 18:31:28.744759: val_loss -0.6986
2025-01-20 18:31:28.744837: Pseudo dice [np.float32(0.756), np.float32(0.761), np.float32(0.8587), np.float32(0.7587), np.float32(0.8881), np.float32(0.7996)]
2025-01-20 18:31:28.744872: Epoch time: 47.75 s
2025-01-20 18:31:28.744895: Yayy! New best EMA pseudo Dice: 0.7991999983787537
2025-01-20 18:31:29.592935:
2025-01-20 18:31:29.593168: Epoch 220
2025-01-20 18:31:29.593250: Current learning rate: 0.008
2025-01-20 18:32:17.311510: train_loss -0.6966
2025-01-20 18:32:17.346693: val_loss -0.6796
2025-01-20 18:32:17.346756: Pseudo dice [np.float32(0.7679), np.float32(0.7832), np.float32(0.8543), np.float32(0.7363), np.float32(0.8832), np.float32(0.7765)]
2025-01-20 18:32:17.346801: Epoch time: 47.72 s
2025-01-20 18:32:17.346825: Yayy! New best EMA pseudo Dice: 0.7993000149726868
2025-01-20 18:32:18.193848:
2025-01-20 18:32:18.229214: Epoch 221
2025-01-20 18:32:18.229325: Current learning rate: 0.00799
2025-01-20 18:33:05.979684: train_loss -0.7028
2025-01-20 18:33:06.014797: val_loss -0.7008
2025-01-20 18:33:06.014852: Pseudo dice [np.float32(0.7422), np.float32(0.7282), np.float32(0.8583), np.float32(0.7462), np.float32(0.879), np.float32(0.7775)]
2025-01-20 18:33:06.014891: Epoch time: 47.79 s
2025-01-20 18:33:06.475547:
2025-01-20 18:33:06.509993: Epoch 222
2025-01-20 18:33:06.510067: Current learning rate: 0.00798
2025-01-20 18:33:54.231888: train_loss -0.7151
2025-01-20 18:33:54.266980: val_loss -0.714
2025-01-20 18:33:54.267050: Pseudo dice [np.float32(0.763), np.float32(0.7787), np.float32(0.8623), np.float32(0.7493), np.float32(0.8949), np.float32(0.7721)]