Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 17:37:27.886544: Current learning rate: 0.00861
2025-01-20 17:38:15.669905: train_loss -0.7037
2025-01-20 17:38:15.704959: val_loss -0.7035
2025-01-20 17:38:15.705019: Pseudo dice [np.float32(0.7452), np.float32(0.7506), np.float32(0.85), np.float32(0.7355), np.float32(0.8866), np.float32(0.7901)]
2025-01-20 17:38:15.705055: Epoch time: 47.78 s
2025-01-20 17:38:16.178490:
2025-01-20 17:38:16.212951: Epoch 154
2025-01-20 17:38:16.213014: Current learning rate: 0.0086
2025-01-20 17:39:03.980297: train_loss -0.7115
2025-01-20 17:39:04.015439: val_loss -0.7006
2025-01-20 17:39:04.015506: Pseudo dice [np.float32(0.7503), np.float32(0.7696), np.float32(0.8508), np.float32(0.7596), np.float32(0.8819), np.float32(0.7626)]
2025-01-20 17:39:04.015552: Epoch time: 47.8 s
2025-01-20 17:39:04.015579: Yayy! New best EMA pseudo Dice: 0.7940999865531921
2025-01-20 17:39:04.871447:
2025-01-20 17:39:04.906816: Epoch 155
2025-01-20 17:39:04.906879: Current learning rate: 0.00859
2025-01-20 17:39:52.643945: train_loss -0.7181
2025-01-20 17:39:52.679006: val_loss -0.7054
2025-01-20 17:39:52.679060: Pseudo dice [np.float32(0.755), np.float32(0.7631), np.float32(0.8556), np.float32(0.7511), np.float32(0.8813), np.float32(0.7583)]
2025-01-20 17:39:52.679097: Epoch time: 47.77 s
2025-01-20 17:39:52.679124: Yayy! New best EMA pseudo Dice: 0.7940999865531921
2025-01-20 17:39:53.531720:
2025-01-20 17:39:53.535583: Epoch 156
2025-01-20 17:39:53.535666: Current learning rate: 0.00858
2025-01-20 17:40:41.336052: train_loss -0.6967
2025-01-20 17:40:41.371214: val_loss -0.6977
2025-01-20 17:40:41.371268: Pseudo dice [np.float32(0.7592), np.float32(0.7671), np.float32(0.8539), np.float32(0.749), np.float32(0.8549), np.float32(0.7713)]
2025-01-20 17:40:41.371303: Epoch time: 47.8 s
2025-01-20 17:40:41.846150:
2025-01-20 17:40:41.880520: Epoch 157
2025-01-20 17:40:41.880605: Current learning rate: 0.00858
2025-01-20 17:41:29.618766: train_loss -0.6923
2025-01-20 17:41:29.653895: val_loss -0.6822
2025-01-20 17:41:29.653949: Pseudo dice [np.float32(0.7478), np.float32(0.7538), np.float32(0.8567), np.float32(0.724), np.float32(0.8906), np.float32(0.7701)]
2025-01-20 17:41:29.653984: Epoch time: 47.77 s
2025-01-20 17:41:30.125671:
2025-01-20 17:41:30.160145: Epoch 158
2025-01-20 17:41:30.160230: Current learning rate: 0.00857
2025-01-20 17:42:17.926205: train_loss -0.6991
2025-01-20 17:42:17.961324: val_loss -0.6742
2025-01-20 17:42:17.961379: Pseudo dice [np.float32(0.7424), np.float32(0.7487), np.float32(0.8508), np.float32(0.726), np.float32(0.8844), np.float32(0.7522)]
2025-01-20 17:42:17.961418: Epoch time: 47.8 s
2025-01-20 17:42:18.437269:
2025-01-20 17:42:18.471745: Epoch 159
2025-01-20 17:42:18.471807: Current learning rate: 0.00856
2025-01-20 17:43:06.224250: train_loss -0.7039
2025-01-20 17:43:06.259376: val_loss -0.6949
2025-01-20 17:43:06.259431: Pseudo dice [np.float32(0.7434), np.float32(0.7602), np.float32(0.8563), np.float32(0.726), np.float32(0.892), np.float32(0.7552)]
2025-01-20 17:43:06.259469: Epoch time: 47.79 s
2025-01-20 17:43:06.733448:
2025-01-20 17:43:06.767899: Epoch 160
2025-01-20 17:43:06.767961: Current learning rate: 0.00855
2025-01-20 17:43:54.556174: train_loss -0.7017
2025-01-20 17:43:54.591177: val_loss -0.6901
2025-01-20 17:43:54.591232: Pseudo dice [np.float32(0.7545), np.float32(0.7445), np.float32(0.8573), np.float32(0.7156), np.float32(0.8868), np.float32(0.7632)]
2025-01-20 17:43:54.591268: Epoch time: 47.82 s
2025-01-20 17:43:55.177056:
2025-01-20 17:43:55.211518: Epoch 161
2025-01-20 17:43:55.211585: Current learning rate: 0.00854
2025-01-20 17:44:43.025014: train_loss -0.7054
2025-01-20 17:44:43.059979: val_loss -0.7052
2025-01-20 17:44:43.060035: Pseudo dice [np.float32(0.7617), np.float32(0.7659), np.float32(0.8547), np.float32(0.747), np.float32(0.8827), np.float32(0.7692)]
2025-01-20 17:44:43.060071: Epoch time: 47.85 s
2025-01-20 17:44:43.530961:
2025-01-20 17:44:43.565452: Epoch 162
2025-01-20 17:44:43.565514: Current learning rate: 0.00853
2025-01-20 17:45:31.327577: train_loss -0.709
2025-01-20 17:45:31.362679: val_loss -0.6992
2025-01-20 17:45:31.362742: Pseudo dice [np.float32(0.7505), np.float32(0.7437), np.float32(0.8532), np.float32(0.7362), np.float32(0.8878), np.float32(0.7704)]
2025-01-20 17:45:31.362787: Epoch time: 47.8 s
2025-01-20 17:45:31.835546:
2025-01-20 17:45:31.869992: Epoch 163
2025-01-20 17:45:31.870055: Current learning rate: 0.00852
2025-01-20 17:46:19.661856: train_loss -0.7103
2025-01-20 17:46:19.696988: val_loss -0.6948
2025-01-20 17:46:19.697040: Pseudo dice [np.float32(0.7658), np.float32(0.7803), np.float32(0.8556), np.float32(0.7476), np.float32(0.8747), np.float32(0.7704)]
2025-01-20 17:46:19.697077: Epoch time: 47.83 s
2025-01-20 17:46:20.171366:
2025-01-20 17:46:20.205842: Epoch 164
2025-01-20 17:46:20.205932: Current learning rate: 0.00851
2025-01-20 17:47:07.986326: train_loss -0.6857
2025-01-20 17:47:08.021344: val_loss -0.7022
2025-01-20 17:47:08.021401: Pseudo dice [np.float32(0.7433), np.float32(0.7419), np.float32(0.8538), np.float32(0.7202), np.float32(0.8835), np.float32(0.7837)]
2025-01-20 17:47:08.021446: Epoch time: 47.82 s
2025-01-20 17:47:08.486682:
2025-01-20 17:47:08.521206: Epoch 165
2025-01-20 17:47:08.521270: Current learning rate: 0.0085
2025-01-20 17:47:56.311008: train_loss -0.7039
2025-01-20 17:47:56.346122: val_loss -0.6921
2025-01-20 17:47:56.346177: Pseudo dice [np.float32(0.7476), np.float32(0.7499), np.float32(0.8571), np.float32(0.753), np.float32(0.8836), np.float32(0.7881)]
2025-01-20 17:47:56.346214: Epoch time: 47.82 s
2025-01-20 17:47:56.812124:
2025-01-20 17:47:56.846598: Epoch 166
2025-01-20 17:47:56.846688: Current learning rate: 0.00849
2025-01-20 17:48:44.581260: train_loss -0.6989
2025-01-20 17:48:44.616382: val_loss -0.6809
2025-01-20 17:48:44.616446: Pseudo dice [np.float32(0.7472), np.float32(0.7167), np.float32(0.8514), np.float32(0.7396), np.float32(0.8738), np.float32(0.7702)]
2025-01-20 17:48:44.616482: Epoch time: 47.77 s
2025-01-20 17:48:45.082270:
2025-01-20 17:48:45.116666: Epoch 167