Datasets:
ngaggion
/
ChronoRoot_nnUNet

Dataset card Data Studio Files Community
1
text
stringlengths
0
1.16k
2025-01-20 15:51:26.660055: Epoch 22
2025-01-20 15:51:26.660140: Current learning rate: 0.0098
2025-01-20 15:52:14.264678: train_loss -0.6214
2025-01-20 15:52:14.299847: val_loss -0.6475
2025-01-20 15:52:14.299966: Pseudo dice [np.float32(0.7123), np.float32(0.7098), np.float32(0.8293), np.float32(0.6769), np.float32(0.8608), np.float32(0.7364)]
2025-01-20 15:52:14.300035: Epoch time: 47.61 s
2025-01-20 15:52:14.300070: Yayy! New best EMA pseudo Dice: 0.6312000155448914
2025-01-20 15:52:15.135537:
2025-01-20 15:52:15.170798: Epoch 23
2025-01-20 15:52:15.170874: Current learning rate: 0.00979
2025-01-20 15:53:02.820495: train_loss -0.635
2025-01-20 15:53:02.855600: val_loss -0.6412
2025-01-20 15:53:02.855655: Pseudo dice [np.float32(0.7112), np.float32(0.7412), np.float32(0.8264), np.float32(0.6627), np.float32(0.8471), np.float32(0.7096)]
2025-01-20 15:53:02.855694: Epoch time: 47.69 s
2025-01-20 15:53:02.855722: Yayy! New best EMA pseudo Dice: 0.6430000066757202
2025-01-20 15:53:03.688585:
2025-01-20 15:53:03.688769: Epoch 24
2025-01-20 15:53:03.688843: Current learning rate: 0.00978
2025-01-20 15:53:51.382452: train_loss -0.6228
2025-01-20 15:53:51.417610: val_loss -0.6369
2025-01-20 15:53:51.417665: Pseudo dice [np.float32(0.7176), np.float32(0.7145), np.float32(0.8351), np.float32(0.6745), np.float32(0.8647), np.float32(0.7008)]
2025-01-20 15:53:51.417749: Epoch time: 47.69 s
2025-01-20 15:53:51.417779: Yayy! New best EMA pseudo Dice: 0.6539000272750854
2025-01-20 15:53:52.259781:
2025-01-20 15:53:52.295186: Epoch 25
2025-01-20 15:53:52.295275: Current learning rate: 0.00977
2025-01-20 15:54:39.975120: train_loss -0.6267
2025-01-20 15:54:40.010238: val_loss -0.6362
2025-01-20 15:54:40.010294: Pseudo dice [np.float32(0.7163), np.float32(0.7333), np.float32(0.8233), np.float32(0.6743), np.float32(0.8629), np.float32(0.7117)]
2025-01-20 15:54:40.010331: Epoch time: 47.72 s
2025-01-20 15:54:40.010352: Yayy! New best EMA pseudo Dice: 0.6638000011444092
2025-01-20 15:54:40.944248:
2025-01-20 15:54:40.947342: Epoch 26
2025-01-20 15:54:40.947447: Current learning rate: 0.00977
2025-01-20 15:55:28.662801: train_loss -0.6286
2025-01-20 15:55:28.697967: val_loss -0.6251
2025-01-20 15:55:28.698028: Pseudo dice [np.float32(0.7059), np.float32(0.6835), np.float32(0.8284), np.float32(0.6223), np.float32(0.8581), np.float32(0.7055)]
2025-01-20 15:55:28.698064: Epoch time: 47.72 s
2025-01-20 15:55:28.698086: Yayy! New best EMA pseudo Dice: 0.670799970626831
2025-01-20 15:55:29.529037:
2025-01-20 15:55:29.529130: Epoch 27
2025-01-20 15:55:29.529212: Current learning rate: 0.00976
2025-01-20 15:56:17.207110: train_loss -0.6346
2025-01-20 15:56:17.242345: val_loss -0.6465
2025-01-20 15:56:17.242488: Pseudo dice [np.float32(0.7221), np.float32(0.7626), np.float32(0.835), np.float32(0.6936), np.float32(0.866), np.float32(0.7492)]
2025-01-20 15:56:17.242524: Epoch time: 47.68 s
2025-01-20 15:56:17.242548: Yayy! New best EMA pseudo Dice: 0.680899977684021
2025-01-20 15:56:18.077879:
2025-01-20 15:56:18.113196: Epoch 28
2025-01-20 15:56:18.113269: Current learning rate: 0.00975
2025-01-20 15:57:05.804224: train_loss -0.6328
2025-01-20 15:57:05.861735: val_loss -0.6195
2025-01-20 15:57:05.861793: Pseudo dice [np.float32(0.6927), np.float32(0.6873), np.float32(0.8378), np.float32(0.6513), np.float32(0.8538), np.float32(0.7119)]
2025-01-20 15:57:05.861832: Epoch time: 47.73 s
2025-01-20 15:57:05.861854: Yayy! New best EMA pseudo Dice: 0.6866999864578247
2025-01-20 15:57:06.702485:
2025-01-20 15:57:06.703529: Epoch 29
2025-01-20 15:57:06.703617: Current learning rate: 0.00974
2025-01-20 15:57:54.382106: train_loss -0.6333
2025-01-20 15:57:54.382370: val_loss -0.6243
2025-01-20 15:57:54.382434: Pseudo dice [np.float32(0.718), np.float32(0.7634), np.float32(0.825), np.float32(0.6907), np.float32(0.866), np.float32(0.7469)]
2025-01-20 15:57:54.382479: Epoch time: 47.68 s
2025-01-20 15:57:54.382508: Yayy! New best EMA pseudo Dice: 0.6948999762535095
2025-01-20 15:57:55.233261:
2025-01-20 15:57:55.268539: Epoch 30
2025-01-20 15:57:55.268612: Current learning rate: 0.00973
2025-01-20 15:58:42.946293: train_loss -0.6361
2025-01-20 15:58:42.981480: val_loss -0.6615
2025-01-20 15:58:42.981542: Pseudo dice [np.float32(0.73), np.float32(0.7726), np.float32(0.8298), np.float32(0.6995), np.float32(0.8574), np.float32(0.7477)]
2025-01-20 15:58:42.981589: Epoch time: 47.71 s
2025-01-20 15:58:42.981630: Yayy! New best EMA pseudo Dice: 0.7027000188827515
2025-01-20 15:58:43.829051:
2025-01-20 15:58:43.831903: Epoch 31
2025-01-20 15:58:43.831976: Current learning rate: 0.00972
2025-01-20 15:59:31.490842: train_loss -0.6526
2025-01-20 15:59:31.490938: val_loss -0.6463
2025-01-20 15:59:31.490988: Pseudo dice [np.float32(0.7231), np.float32(0.7441), np.float32(0.8345), np.float32(0.7282), np.float32(0.8694), np.float32(0.7504)]
2025-01-20 15:59:31.491041: Epoch time: 47.66 s
2025-01-20 15:59:31.491077: Yayy! New best EMA pseudo Dice: 0.7099000215530396
2025-01-20 15:59:32.270681:
2025-01-20 15:59:32.270921: Epoch 32
2025-01-20 15:59:32.270978: Current learning rate: 0.00971
2025-01-20 16:00:19.966604: train_loss -0.6428
2025-01-20 16:00:20.001844: val_loss -0.6424
2025-01-20 16:00:20.001918: Pseudo dice [np.float32(0.7292), np.float32(0.7306), np.float32(0.8297), np.float32(0.6996), np.float32(0.8674), np.float32(0.7486)]
2025-01-20 16:00:20.001978: Epoch time: 47.7 s
2025-01-20 16:00:20.002008: Yayy! New best EMA pseudo Dice: 0.7156999707221985
2025-01-20 16:00:20.849670:
2025-01-20 16:00:20.853057: Epoch 33
2025-01-20 16:00:20.853143: Current learning rate: 0.0097
2025-01-20 16:01:08.561900: train_loss -0.6466
2025-01-20 16:01:08.639465: val_loss -0.6439
2025-01-20 16:01:08.639521: Pseudo dice [np.float32(0.717), np.float32(0.7181), np.float32(0.8348), np.float32(0.6454), np.float32(0.8569), np.float32(0.7258)]
2025-01-20 16:01:08.639557: Epoch time: 47.71 s
2025-01-20 16:01:08.639594: Yayy! New best EMA pseudo Dice: 0.7190999984741211
2025-01-20 16:01:09.587070:
2025-01-20 16:01:09.622417: Epoch 34
2025-01-20 16:01:09.622521: Current learning rate: 0.00969
2025-01-20 16:01:57.316416: train_loss -0.649
2025-01-20 16:01:57.351414: val_loss -0.643