Upload fold_all/training_log_2026_3_3_21_33_06.txt with huggingface_hub
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fold_all/training_log_2026_3_3_21_33_06.txt
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|
| 1 |
+
|
| 2 |
+
#######################################################################
|
| 3 |
+
Please cite the following paper when using nnU-Net:
|
| 4 |
+
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
|
| 5 |
+
#######################################################################
|
| 6 |
+
|
| 7 |
+
2026-03-03 21:33:07.826505: Using torch.compile...
|
| 8 |
+
2026-03-03 21:33:09.944130: do_dummy_2d_data_aug: False
|
| 9 |
+
|
| 10 |
+
This is the configuration used by this training:
|
| 11 |
+
Configuration name: 3d_fullres
|
| 12 |
+
{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [320.0, 314.0, 314.0], 'spacing': [7.91, 7.91, 7.91], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}
|
| 13 |
+
|
| 14 |
+
These are the global plan.json settings:
|
| 15 |
+
{'dataset_name': 'Dataset011_Vesuvius', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [7.91, 7.91, 7.91], 'original_median_shape_after_transp': [320, 314, 314], 'image_reader_writer': 'Tiff3DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 87.54424285888672, 'median': 81.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 212.0, 'std': 47.74376678466797}}}
|
| 16 |
+
|
| 17 |
+
2026-03-03 21:33:15.486389: Unable to plot network architecture: nnUNet_compile is enabled!
|
| 18 |
+
2026-03-03 21:33:15.997430:
|
| 19 |
+
2026-03-03 21:33:15.997630: Epoch 0
|
| 20 |
+
2026-03-03 21:33:15.997824: Current learning rate: 0.01
|
| 21 |
+
2026-03-03 21:38:19.758334: train_loss 0.7206
|
| 22 |
+
2026-03-03 21:38:19.770421: val_loss 0.632
|
| 23 |
+
2026-03-03 21:38:19.770512: Pseudo dice [np.float32(0.0), np.float32(0.6503)]
|
| 24 |
+
2026-03-03 21:38:19.770604: Epoch time: 303.76 s
|
| 25 |
+
2026-03-03 21:38:19.770663: Yayy! New best EMA pseudo Dice: 0.32519999146461487
|
| 26 |
+
2026-03-03 21:38:21.803894:
|
| 27 |
+
2026-03-03 21:38:21.804168: Epoch 1
|
| 28 |
+
2026-03-03 21:38:21.804275: Current learning rate: 0.00995
|
| 29 |
+
2026-03-03 21:40:44.628085: train_loss 0.6082
|
| 30 |
+
2026-03-03 21:40:44.655137: val_loss 0.6308
|
| 31 |
+
2026-03-03 21:40:44.655261: Pseudo dice [np.float32(0.0), np.float32(0.606)]
|
| 32 |
+
2026-03-03 21:40:44.655335: Epoch time: 142.83 s
|
| 33 |
+
2026-03-03 21:40:46.114195:
|
| 34 |
+
2026-03-03 21:40:46.114333: Epoch 2
|
| 35 |
+
2026-03-03 21:40:46.114430: Current learning rate: 0.00991
|
| 36 |
+
2026-03-03 21:43:16.634641: train_loss 0.6027
|
| 37 |
+
2026-03-03 21:43:16.745428: val_loss 0.5626
|
| 38 |
+
2026-03-03 21:43:16.748434: Pseudo dice [np.float32(0.0), np.float32(0.6511)]
|
| 39 |
+
2026-03-03 21:43:16.769326: Epoch time: 150.48 s
|
| 40 |
+
2026-03-03 21:43:19.591169:
|
| 41 |
+
2026-03-03 21:43:19.591350: Epoch 3
|
| 42 |
+
2026-03-03 21:43:19.591459: Current learning rate: 0.00986
|
| 43 |
+
2026-03-03 21:45:43.843567: train_loss 0.592
|
| 44 |
+
2026-03-03 21:45:44.024111: val_loss 0.612
|
| 45 |
+
2026-03-03 21:45:44.024251: Pseudo dice [np.float32(0.0), np.float32(0.5253)]
|
| 46 |
+
2026-03-03 21:45:44.026057: Epoch time: 144.25 s
|
| 47 |
+
2026-03-03 21:45:46.089931:
|
| 48 |
+
2026-03-03 21:45:46.090119: Epoch 4
|
| 49 |
+
2026-03-03 21:45:46.090251: Current learning rate: 0.00982
|
| 50 |
+
2026-03-03 21:48:09.963689: train_loss 0.5598
|
| 51 |
+
2026-03-03 21:48:10.086342: val_loss 0.5769
|
| 52 |
+
2026-03-03 21:48:10.086823: Pseudo dice [np.float32(0.0), np.float32(0.622)]
|
| 53 |
+
2026-03-03 21:48:10.104435: Epoch time: 143.85 s
|
| 54 |
+
2026-03-03 21:48:13.289501:
|
| 55 |
+
2026-03-03 21:48:13.289801: Epoch 5
|
| 56 |
+
2026-03-03 21:48:13.289953: Current learning rate: 0.00977
|
| 57 |
+
2026-03-03 21:50:36.743212: train_loss 0.581
|
| 58 |
+
2026-03-03 21:50:36.802017: val_loss 0.6232
|
| 59 |
+
2026-03-03 21:50:36.802593: Pseudo dice [np.float32(0.0), np.float32(0.6144)]
|
| 60 |
+
2026-03-03 21:50:36.821261: Epoch time: 143.46 s
|
| 61 |
+
2026-03-03 21:50:39.016676:
|
| 62 |
+
2026-03-03 21:50:39.016881: Epoch 6
|
| 63 |
+
2026-03-03 21:50:39.016983: Current learning rate: 0.00973
|
| 64 |
+
2026-03-03 21:53:03.683199: train_loss 0.5716
|
| 65 |
+
2026-03-03 21:53:03.769712: val_loss 0.5418
|
| 66 |
+
2026-03-03 21:53:03.771623: Pseudo dice [np.float32(0.0041), np.float32(0.6659)]
|
| 67 |
+
2026-03-03 21:53:03.783466: Epoch time: 144.66 s
|
| 68 |
+
2026-03-03 21:53:06.240991:
|
| 69 |
+
2026-03-03 21:53:06.241260: Epoch 7
|
| 70 |
+
2026-03-03 21:53:06.241361: Current learning rate: 0.00968
|
| 71 |
+
2026-03-03 21:55:30.237941: train_loss 0.5523
|
| 72 |
+
2026-03-03 21:55:30.311615: val_loss 0.5977
|
| 73 |
+
2026-03-03 21:55:30.311910: Pseudo dice [np.float32(0.008), np.float32(0.5884)]
|
| 74 |
+
2026-03-03 21:55:30.321557: Epoch time: 144.0 s
|
| 75 |
+
2026-03-03 21:55:32.470290:
|
| 76 |
+
2026-03-03 21:55:32.470516: Epoch 8
|
| 77 |
+
2026-03-03 21:55:32.470618: Current learning rate: 0.00964
|
| 78 |
+
2026-03-03 21:58:00.042668: train_loss 0.5684
|
| 79 |
+
2026-03-03 21:58:00.063523: val_loss 0.6006
|
| 80 |
+
2026-03-03 21:58:00.063609: Pseudo dice [np.float32(0.0), np.float32(0.5942)]
|
| 81 |
+
2026-03-03 21:58:00.067849: Epoch time: 147.57 s
|
| 82 |
+
2026-03-03 21:58:30.093642:
|
| 83 |
+
2026-03-03 21:58:30.093920: Epoch 9
|
| 84 |
+
2026-03-03 21:58:30.094021: Current learning rate: 0.00959
|
| 85 |
+
2026-03-03 22:00:51.261670: train_loss 0.5672
|
| 86 |
+
2026-03-03 22:00:51.347862: val_loss 0.5241
|
| 87 |
+
2026-03-03 22:00:51.348168: Pseudo dice [np.float32(0.0), np.float32(0.6627)]
|
| 88 |
+
2026-03-03 22:00:51.352621: Epoch time: 141.17 s
|
| 89 |
+
2026-03-03 22:00:53.553246:
|
| 90 |
+
2026-03-03 22:00:53.553517: Epoch 10
|
| 91 |
+
2026-03-03 22:00:53.553624: Current learning rate: 0.00955
|
| 92 |
+
2026-03-03 22:03:23.309453: train_loss 0.5417
|
| 93 |
+
2026-03-03 22:03:23.327398: val_loss 0.5476
|
| 94 |
+
2026-03-03 22:03:23.327939: Pseudo dice [np.float32(0.0), np.float32(0.6666)]
|
| 95 |
+
2026-03-03 22:03:23.330661: Epoch time: 149.76 s
|
| 96 |
+
2026-03-03 22:03:25.845042:
|
| 97 |
+
2026-03-03 22:03:25.845343: Epoch 11
|
| 98 |
+
2026-03-03 22:03:25.845448: Current learning rate: 0.0095
|
| 99 |
+
2026-03-03 22:05:52.669398: train_loss 0.5637
|
| 100 |
+
2026-03-03 22:05:52.690779: val_loss 0.565
|
| 101 |
+
2026-03-03 22:05:52.691271: Pseudo dice [np.float32(0.0005), np.float32(0.61)]
|
| 102 |
+
2026-03-03 22:05:52.695226: Epoch time: 146.82 s
|
| 103 |
+
2026-03-03 22:05:55.059874:
|
| 104 |
+
2026-03-03 22:05:55.060850: Epoch 12
|
| 105 |
+
2026-03-03 22:05:55.060950: Current learning rate: 0.00946
|
| 106 |
+
2026-03-03 22:08:17.076872: train_loss 0.5494
|
| 107 |
+
2026-03-03 22:08:17.110195: val_loss 0.5329
|
| 108 |
+
2026-03-03 22:08:17.112072: Pseudo dice [np.float32(0.0), np.float32(0.6729)]
|
| 109 |
+
2026-03-03 22:08:17.126230: Epoch time: 142.02 s
|
| 110 |
+
2026-03-03 22:08:19.318452:
|
| 111 |
+
2026-03-03 22:08:19.318674: Epoch 13
|
| 112 |
+
2026-03-03 22:08:19.318771: Current learning rate: 0.00941
|
| 113 |
+
2026-03-03 22:10:43.486225: train_loss 0.5526
|
| 114 |
+
2026-03-03 22:10:43.557377: val_loss 0.534
|
| 115 |
+
2026-03-03 22:10:43.557942: Pseudo dice [np.float32(0.0231), np.float32(0.6611)]
|
| 116 |
+
2026-03-03 22:10:43.562134: Epoch time: 144.16 s
|
| 117 |
+
2026-03-03 22:10:46.025419:
|
| 118 |
+
2026-03-03 22:10:46.025663: Epoch 14
|
| 119 |
+
2026-03-03 22:10:46.025771: Current learning rate: 0.00937
|
| 120 |
+
2026-03-03 22:13:06.957939: train_loss 0.5462
|
| 121 |
+
2026-03-03 22:13:06.982514: val_loss 0.5188
|
| 122 |
+
2026-03-03 22:13:06.984670: Pseudo dice [np.float32(0.0006), np.float32(0.669)]
|
| 123 |
+
2026-03-03 22:13:06.988953: Epoch time: 140.93 s
|
| 124 |
+
2026-03-03 22:13:10.308714:
|
| 125 |
+
2026-03-03 22:13:10.308953: Epoch 15
|
| 126 |
+
2026-03-03 22:13:10.309388: Current learning rate: 0.00932
|
| 127 |
+
2026-03-03 22:15:37.509683: train_loss 0.5147
|
| 128 |
+
2026-03-03 22:15:37.531674: val_loss 0.5221
|
| 129 |
+
2026-03-03 22:15:37.532182: Pseudo dice [np.float32(0.0024), np.float32(0.6707)]
|
| 130 |
+
2026-03-03 22:15:37.535618: Epoch time: 147.2 s
|
| 131 |
+
2026-03-03 22:15:40.184778:
|
| 132 |
+
2026-03-03 22:15:40.185025: Epoch 16
|
| 133 |
+
2026-03-03 22:15:40.185125: Current learning rate: 0.00928
|
| 134 |
+
2026-03-03 22:18:00.180995: train_loss 0.5623
|
| 135 |
+
2026-03-03 22:18:00.206226: val_loss 0.5395
|
| 136 |
+
2026-03-03 22:18:00.207909: Pseudo dice [np.float32(0.0064), np.float32(0.574)]
|
| 137 |
+
2026-03-03 22:18:00.360198: Epoch time: 140.0 s
|
| 138 |
+
2026-03-03 22:18:02.840868:
|
| 139 |
+
2026-03-03 22:18:02.841058: Epoch 17
|
| 140 |
+
2026-03-03 22:18:02.841186: Current learning rate: 0.00923
|
| 141 |
+
2026-03-03 22:20:26.416060: train_loss 0.5281
|
| 142 |
+
2026-03-03 22:20:26.432789: val_loss 0.5801
|
| 143 |
+
2026-03-03 22:20:26.432869: Pseudo dice [np.float32(0.0), np.float32(0.6409)]
|
| 144 |
+
2026-03-03 22:20:26.435180: Epoch time: 143.57 s
|
| 145 |
+
2026-03-03 22:20:28.777847:
|
| 146 |
+
2026-03-03 22:20:28.778032: Epoch 18
|
| 147 |
+
2026-03-03 22:20:28.778167: Current learning rate: 0.00919
|
| 148 |
+
2026-03-03 22:23:09.165542: train_loss 0.506
|
| 149 |
+
2026-03-03 22:23:09.285186: val_loss 0.5366
|
| 150 |
+
2026-03-03 22:23:09.289048: Pseudo dice [np.float32(0.0227), np.float32(0.0449)]
|
| 151 |
+
2026-03-03 22:23:09.350798: Epoch time: 160.35 s
|
| 152 |
+
2026-03-03 22:23:12.097159:
|
| 153 |
+
2026-03-03 22:23:12.097376: Epoch 19
|
| 154 |
+
2026-03-03 22:23:12.097478: Current learning rate: 0.00914
|
| 155 |
+
2026-03-03 22:25:35.135089: train_loss 0.5638
|
| 156 |
+
2026-03-03 22:25:35.226205: val_loss 0.5205
|
| 157 |
+
2026-03-03 22:25:35.227111: Pseudo dice [np.float32(0.0), np.float32(0.6564)]
|
| 158 |
+
2026-03-03 22:25:35.239418: Epoch time: 143.04 s
|
| 159 |
+
2026-03-03 22:25:38.327005:
|
| 160 |
+
2026-03-03 22:25:38.327282: Epoch 20
|
| 161 |
+
2026-03-03 22:25:38.327381: Current learning rate: 0.0091
|
| 162 |
+
2026-03-03 22:27:59.983615: train_loss 0.4965
|
| 163 |
+
2026-03-03 22:28:00.097574: val_loss 0.5341
|
| 164 |
+
2026-03-03 22:28:00.098702: Pseudo dice [np.float32(0.0039), np.float32(0.6927)]
|
| 165 |
+
2026-03-03 22:28:00.110992: Epoch time: 141.65 s
|
| 166 |
+
2026-03-03 22:28:02.590075:
|
| 167 |
+
2026-03-03 22:28:02.590327: Epoch 21
|
| 168 |
+
2026-03-03 22:28:02.590429: Current learning rate: 0.00905
|
| 169 |
+
2026-03-03 22:30:38.604162: train_loss 0.5156
|
| 170 |
+
2026-03-03 22:30:38.630687: val_loss 0.534
|
| 171 |
+
2026-03-03 22:30:38.631868: Pseudo dice [np.float32(0.0012), np.float32(0.6625)]
|
| 172 |
+
2026-03-03 22:30:38.636516: Epoch time: 155.92 s
|
| 173 |
+
2026-03-03 22:30:41.487096:
|
| 174 |
+
2026-03-03 22:30:41.487241: Epoch 22
|
| 175 |
+
2026-03-03 22:30:41.487345: Current learning rate: 0.009
|
| 176 |
+
2026-03-03 22:33:01.817354: train_loss 0.5092
|
| 177 |
+
2026-03-03 22:33:01.833530: val_loss 0.5826
|
| 178 |
+
2026-03-03 22:33:01.834038: Pseudo dice [np.float32(0.0028), np.float32(0.6239)]
|
| 179 |
+
2026-03-03 22:33:01.852883: Epoch time: 140.33 s
|
| 180 |
+
2026-03-03 22:33:31.355509:
|
| 181 |
+
2026-03-03 22:33:31.355702: Epoch 23
|
| 182 |
+
2026-03-03 22:33:31.355811: Current learning rate: 0.00896
|
| 183 |
+
2026-03-03 22:35:51.602790: train_loss 0.5356
|
| 184 |
+
2026-03-03 22:35:51.686009: val_loss 0.5387
|
| 185 |
+
2026-03-03 22:35:51.686284: Pseudo dice [np.float32(0.0005), np.float32(0.6691)]
|
| 186 |
+
2026-03-03 22:35:51.698382: Epoch time: 140.24 s
|
| 187 |
+
2026-03-03 22:35:58.279609:
|
| 188 |
+
2026-03-03 22:35:58.279739: Epoch 24
|
| 189 |
+
2026-03-03 22:35:58.279837: Current learning rate: 0.00891
|
| 190 |
+
2026-03-03 22:38:29.312170: train_loss 0.5224
|
| 191 |
+
2026-03-03 22:38:29.443477: val_loss 0.5522
|
| 192 |
+
2026-03-03 22:38:29.446812: Pseudo dice [np.float32(0.0), np.float32(0.6467)]
|
| 193 |
+
2026-03-03 22:38:29.466471: Epoch time: 151.0 s
|
| 194 |
+
2026-03-03 22:38:33.344309:
|
| 195 |
+
2026-03-03 22:38:33.351250: Epoch 25
|
| 196 |
+
2026-03-03 22:38:33.351359: Current learning rate: 0.00887
|
| 197 |
+
2026-03-03 22:40:53.092479: train_loss 0.5133
|
| 198 |
+
2026-03-03 22:40:53.139898: val_loss 0.5603
|
| 199 |
+
2026-03-03 22:40:53.140381: Pseudo dice [np.float32(0.0126), np.float32(0.5338)]
|
| 200 |
+
2026-03-03 22:40:53.148964: Epoch time: 139.75 s
|
| 201 |
+
2026-03-03 22:40:55.077281:
|
| 202 |
+
2026-03-03 22:40:55.077526: Epoch 26
|
| 203 |
+
2026-03-03 22:40:55.077627: Current learning rate: 0.00882
|
| 204 |
+
2026-03-03 22:43:22.080101: train_loss 0.5197
|
| 205 |
+
2026-03-03 22:43:22.190693: val_loss 0.5438
|
| 206 |
+
2026-03-03 22:43:22.193531: Pseudo dice [np.float32(0.0), np.float32(0.6793)]
|
| 207 |
+
2026-03-03 22:43:22.212313: Epoch time: 146.96 s
|
| 208 |
+
2026-03-03 22:43:25.690314:
|
| 209 |
+
2026-03-03 22:43:25.695249: Epoch 27
|
| 210 |
+
2026-03-03 22:43:25.695364: Current learning rate: 0.00878
|
| 211 |
+
2026-03-03 22:45:42.065329: train_loss 0.5386
|
| 212 |
+
2026-03-03 22:45:42.142742: val_loss 0.5416
|
| 213 |
+
2026-03-03 22:45:42.144270: Pseudo dice [np.float32(0.0318), np.float32(0.6219)]
|
| 214 |
+
2026-03-03 22:45:42.155748: Epoch time: 136.36 s
|
| 215 |
+
2026-03-03 22:45:45.529598:
|
| 216 |
+
2026-03-03 22:45:45.532436: Epoch 28
|
| 217 |
+
2026-03-03 22:45:45.532760: Current learning rate: 0.00873
|
| 218 |
+
2026-03-03 22:48:07.415765: train_loss 0.5329
|
| 219 |
+
2026-03-03 22:48:07.538065: val_loss 0.534
|
| 220 |
+
2026-03-03 22:48:07.539602: Pseudo dice [np.float32(0.023), np.float32(0.5084)]
|
| 221 |
+
2026-03-03 22:48:07.568186: Epoch time: 141.87 s
|
| 222 |
+
2026-03-03 22:48:10.124135:
|
| 223 |
+
2026-03-03 22:48:10.124366: Epoch 29
|
| 224 |
+
2026-03-03 22:48:10.124469: Current learning rate: 0.00868
|
| 225 |
+
2026-03-03 22:50:32.612099: train_loss 0.541
|
| 226 |
+
2026-03-03 22:50:32.766207: val_loss 0.5272
|
| 227 |
+
2026-03-03 22:50:32.768293: Pseudo dice [np.float32(0.0015), np.float32(0.6536)]
|
| 228 |
+
2026-03-03 22:50:32.790195: Epoch time: 142.41 s
|
| 229 |
+
2026-03-03 22:50:35.980449:
|
| 230 |
+
2026-03-03 22:50:35.980667: Epoch 30
|
| 231 |
+
2026-03-03 22:50:35.981412: Current learning rate: 0.00864
|
| 232 |
+
2026-03-03 22:52:59.737009: train_loss 0.5353
|
| 233 |
+
2026-03-03 22:52:59.756337: val_loss 0.5034
|
| 234 |
+
2026-03-03 22:52:59.756486: Pseudo dice [np.float32(0.002), np.float32(0.6781)]
|
| 235 |
+
2026-03-03 22:52:59.760006: Epoch time: 143.76 s
|
| 236 |
+
2026-03-03 22:53:01.797701:
|
| 237 |
+
2026-03-03 22:53:01.797983: Epoch 31
|
| 238 |
+
2026-03-03 22:53:01.798076: Current learning rate: 0.00859
|
| 239 |
+
2026-03-03 22:55:35.435423: train_loss 0.5465
|
| 240 |
+
2026-03-03 22:55:35.459571: val_loss 0.5143
|
| 241 |
+
2026-03-03 22:55:35.460104: Pseudo dice [np.float32(0.012), np.float32(0.6551)]
|
| 242 |
+
2026-03-03 22:55:35.466345: Epoch time: 153.63 s
|
| 243 |
+
2026-03-03 22:55:38.692710:
|
| 244 |
+
2026-03-03 22:55:38.693593: Epoch 32
|
| 245 |
+
2026-03-03 22:55:38.693701: Current learning rate: 0.00855
|
| 246 |
+
2026-03-03 22:57:58.757853: train_loss 0.5381
|
| 247 |
+
2026-03-03 22:57:58.806224: val_loss 0.5969
|
| 248 |
+
2026-03-03 22:57:58.806477: Pseudo dice [np.float32(0.0132), np.float32(0.0052)]
|
| 249 |
+
2026-03-03 22:57:58.810736: Epoch time: 140.06 s
|
| 250 |
+
2026-03-03 22:58:01.448985:
|
| 251 |
+
2026-03-03 22:58:01.449233: Epoch 33
|
| 252 |
+
2026-03-03 22:58:01.449339: Current learning rate: 0.0085
|
| 253 |
+
2026-03-03 23:00:29.995694: train_loss 0.5419
|
| 254 |
+
2026-03-03 23:00:30.111049: val_loss 0.5703
|
| 255 |
+
2026-03-03 23:00:30.111410: Pseudo dice [np.float32(0.0153), np.float32(1e-04)]
|
| 256 |
+
2026-03-03 23:00:30.139433: Epoch time: 148.49 s
|
| 257 |
+
2026-03-03 23:00:34.143376:
|
| 258 |
+
2026-03-03 23:00:34.144203: Epoch 34
|
| 259 |
+
2026-03-03 23:00:34.145017: Current learning rate: 0.00846
|
| 260 |
+
2026-03-03 23:02:53.554609: train_loss 0.5258
|
| 261 |
+
2026-03-03 23:02:53.670291: val_loss 0.5321
|
| 262 |
+
2026-03-03 23:02:53.670653: Pseudo dice [np.float32(0.0739), np.float32(0.4381)]
|
| 263 |
+
2026-03-03 23:02:53.685583: Epoch time: 139.41 s
|
| 264 |
+
2026-03-03 23:02:56.389626:
|
| 265 |
+
2026-03-03 23:02:56.389816: Epoch 35
|
| 266 |
+
2026-03-03 23:02:56.389917: Current learning rate: 0.00841
|
| 267 |
+
2026-03-03 23:05:25.627836: train_loss 0.5236
|
| 268 |
+
2026-03-03 23:05:25.702152: val_loss 0.4999
|
| 269 |
+
2026-03-03 23:05:25.702444: Pseudo dice [np.float32(0.0132), np.float32(0.6828)]
|
| 270 |
+
2026-03-03 23:05:25.721072: Epoch time: 149.24 s
|
| 271 |
+
2026-03-03 23:05:29.167432:
|
| 272 |
+
2026-03-03 23:05:29.167681: Epoch 36
|
| 273 |
+
2026-03-03 23:05:29.167813: Current learning rate: 0.00836
|
| 274 |
+
2026-03-03 23:07:46.373575: train_loss 0.5338
|
| 275 |
+
2026-03-03 23:07:46.515176: val_loss 0.5881
|
| 276 |
+
2026-03-03 23:07:46.515510: Pseudo dice [np.float32(0.0006), np.float32(0.6277)]
|
| 277 |
+
2026-03-03 23:07:46.530992: Epoch time: 137.2 s
|
| 278 |
+
2026-03-03 23:07:49.817466:
|
| 279 |
+
2026-03-03 23:07:49.817757: Epoch 37
|
| 280 |
+
2026-03-03 23:07:49.817863: Current learning rate: 0.00832
|
| 281 |
+
2026-03-03 23:10:18.068836: train_loss 0.5355
|
| 282 |
+
2026-03-03 23:10:18.090895: val_loss 0.5129
|
| 283 |
+
2026-03-03 23:10:18.091071: Pseudo dice [np.float32(0.0188), np.float32(0.6898)]
|
| 284 |
+
2026-03-03 23:10:18.095359: Epoch time: 148.25 s
|
| 285 |
+
2026-03-03 23:10:48.341994:
|
| 286 |
+
2026-03-03 23:10:48.342227: Epoch 38
|
| 287 |
+
2026-03-03 23:10:48.342335: Current learning rate: 0.00827
|
| 288 |
+
2026-03-03 23:13:06.205402: train_loss 0.5116
|
| 289 |
+
2026-03-03 23:13:06.233675: val_loss 0.5308
|
| 290 |
+
2026-03-03 23:13:06.233768: Pseudo dice [np.float32(0.0632), np.float32(0.59)]
|
| 291 |
+
2026-03-03 23:13:06.241675: Epoch time: 137.86 s
|
| 292 |
+
2026-03-03 23:13:08.628527:
|
| 293 |
+
2026-03-03 23:13:08.628752: Epoch 39
|
| 294 |
+
2026-03-03 23:13:08.628858: Current learning rate: 0.00823
|
| 295 |
+
2026-03-03 23:15:47.436576: train_loss 0.5154
|
| 296 |
+
2026-03-03 23:15:47.533607: val_loss 0.5174
|
| 297 |
+
2026-03-03 23:15:47.533773: Pseudo dice [np.float32(0.0313), np.float32(0.6631)]
|
| 298 |
+
2026-03-03 23:15:47.548620: Epoch time: 158.78 s
|
| 299 |
+
2026-03-03 23:15:51.864279:
|
| 300 |
+
2026-03-03 23:15:51.869550: Epoch 40
|
| 301 |
+
2026-03-03 23:15:51.869661: Current learning rate: 0.00818
|
| 302 |
+
2026-03-03 23:18:15.053376: train_loss 0.5111
|
| 303 |
+
2026-03-03 23:18:15.492595: val_loss 0.5331
|
| 304 |
+
2026-03-03 23:18:15.492777: Pseudo dice [np.float32(0.0292), np.float32(0.6422)]
|
| 305 |
+
2026-03-03 23:18:15.518826: Epoch time: 143.17 s
|
| 306 |
+
2026-03-03 23:18:17.597552:
|
| 307 |
+
2026-03-03 23:18:17.599471: Epoch 41
|
| 308 |
+
2026-03-03 23:18:17.599573: Current learning rate: 0.00813
|
| 309 |
+
2026-03-03 23:20:42.334847: train_loss 0.5012
|
| 310 |
+
2026-03-03 23:20:42.524105: val_loss 0.5304
|
| 311 |
+
2026-03-03 23:20:42.524321: Pseudo dice [np.float32(0.0285), np.float32(0.6412)]
|
| 312 |
+
2026-03-03 23:20:42.554501: Epoch time: 144.73 s
|
| 313 |
+
2026-03-03 23:20:45.934673:
|
| 314 |
+
2026-03-03 23:20:45.943098: Epoch 42
|
| 315 |
+
2026-03-03 23:20:45.943233: Current learning rate: 0.00809
|
| 316 |
+
2026-03-03 23:23:10.372324: train_loss 0.5016
|
| 317 |
+
2026-03-03 23:23:10.504625: val_loss 0.5149
|
| 318 |
+
2026-03-03 23:23:10.504799: Pseudo dice [np.float32(0.0081), np.float32(0.6745)]
|
| 319 |
+
2026-03-03 23:23:10.514257: Epoch time: 144.43 s
|
| 320 |
+
2026-03-03 23:23:13.987271:
|
| 321 |
+
2026-03-03 23:23:13.988044: Epoch 43
|
| 322 |
+
2026-03-03 23:23:13.988182: Current learning rate: 0.00804
|
| 323 |
+
2026-03-03 23:25:44.011225: train_loss 0.5203
|
| 324 |
+
2026-03-03 23:25:44.174621: val_loss 0.4824
|
| 325 |
+
2026-03-03 23:25:44.181063: Pseudo dice [np.float32(0.0022), np.float32(0.6969)]
|
| 326 |
+
2026-03-03 23:25:44.203264: Epoch time: 149.97 s
|
| 327 |
+
2026-03-03 23:25:48.083756:
|
| 328 |
+
2026-03-03 23:25:48.083873: Epoch 44
|
| 329 |
+
2026-03-03 23:25:48.083971: Current learning rate: 0.008
|
| 330 |
+
2026-03-03 23:28:12.193308: train_loss 0.5386
|
| 331 |
+
2026-03-03 23:28:12.252196: val_loss 0.5151
|
| 332 |
+
2026-03-03 23:28:12.252477: Pseudo dice [np.float32(0.098), np.float32(0.6314)]
|
| 333 |
+
2026-03-03 23:28:12.257315: Epoch time: 144.11 s
|
| 334 |
+
2026-03-03 23:28:15.273276:
|
| 335 |
+
2026-03-03 23:28:15.273507: Epoch 45
|
| 336 |
+
2026-03-03 23:28:15.273620: Current learning rate: 0.00795
|
| 337 |
+
2026-03-03 23:30:44.038577: train_loss 0.5176
|
| 338 |
+
2026-03-03 23:30:44.063546: val_loss 0.4999
|
| 339 |
+
2026-03-03 23:30:44.064192: Pseudo dice [np.float32(0.017), np.float32(0.6866)]
|
| 340 |
+
2026-03-03 23:30:44.069046: Epoch time: 148.76 s
|
| 341 |
+
2026-03-03 23:30:47.909820:
|
| 342 |
+
2026-03-03 23:30:47.910023: Epoch 46
|
| 343 |
+
2026-03-03 23:30:47.910123: Current learning rate: 0.0079
|
| 344 |
+
2026-03-03 23:33:07.415025: train_loss 0.5102
|
| 345 |
+
2026-03-03 23:33:07.517706: val_loss 0.5072
|
| 346 |
+
2026-03-03 23:33:07.520815: Pseudo dice [np.float32(0.0935), np.float32(0.6499)]
|
| 347 |
+
2026-03-03 23:33:07.524179: Epoch time: 139.5 s
|
| 348 |
+
2026-03-03 23:33:10.005588:
|
| 349 |
+
2026-03-03 23:33:10.005781: Epoch 47
|
| 350 |
+
2026-03-03 23:33:10.005882: Current learning rate: 0.00786
|
| 351 |
+
2026-03-03 23:35:36.718813: train_loss 0.5126
|
| 352 |
+
2026-03-03 23:35:36.813648: val_loss 0.5207
|
| 353 |
+
2026-03-03 23:35:36.813955: Pseudo dice [np.float32(0.0209), np.float32(0.682)]
|
| 354 |
+
2026-03-03 23:35:36.827390: Epoch time: 146.69 s
|
| 355 |
+
2026-03-03 23:35:36.830118: Yayy! New best EMA pseudo Dice: 0.325300008058548
|
| 356 |
+
2026-03-03 23:35:43.483753:
|
| 357 |
+
2026-03-03 23:35:43.483933: Epoch 48
|
| 358 |
+
2026-03-03 23:35:43.484034: Current learning rate: 0.00781
|
| 359 |
+
2026-03-03 23:38:03.709986: train_loss 0.5019
|
| 360 |
+
2026-03-03 23:38:03.769134: val_loss 0.5139
|
| 361 |
+
2026-03-03 23:38:03.769259: Pseudo dice [np.float32(0.0347), np.float32(0.6605)]
|
| 362 |
+
2026-03-03 23:38:03.772019: Epoch time: 140.23 s
|
| 363 |
+
2026-03-03 23:38:03.772245: Yayy! New best EMA pseudo Dice: 0.32749998569488525
|
| 364 |
+
2026-03-03 23:38:10.202998:
|
| 365 |
+
2026-03-03 23:38:10.203267: Epoch 49
|
| 366 |
+
2026-03-03 23:38:10.203369: Current learning rate: 0.00777
|
| 367 |
+
2026-03-03 23:40:41.345560: train_loss 0.5153
|
| 368 |
+
2026-03-03 23:40:41.462390: val_loss 0.505
|
| 369 |
+
2026-03-03 23:40:41.462626: Pseudo dice [np.float32(0.095), np.float32(0.6639)]
|
| 370 |
+
2026-03-03 23:40:41.490546: Epoch time: 151.14 s
|
| 371 |
+
2026-03-03 23:40:43.230243: Yayy! New best EMA pseudo Dice: 0.3327000141143799
|
| 372 |
+
2026-03-03 23:40:48.509203:
|
| 373 |
+
2026-03-03 23:40:48.509964: Epoch 50
|
| 374 |
+
2026-03-03 23:40:48.510065: Current learning rate: 0.00772
|
| 375 |
+
2026-03-03 23:43:14.072112: train_loss 0.5246
|
| 376 |
+
2026-03-03 23:43:14.157112: val_loss 0.5061
|
| 377 |
+
2026-03-03 23:43:14.161056: Pseudo dice [np.float32(0.0185), np.float32(0.6722)]
|
| 378 |
+
2026-03-03 23:43:14.173364: Epoch time: 145.57 s
|
| 379 |
+
2026-03-03 23:43:14.173503: Yayy! New best EMA pseudo Dice: 0.33399999141693115
|
| 380 |
+
2026-03-03 23:43:18.914922:
|
| 381 |
+
2026-03-03 23:43:18.915179: Epoch 51
|
| 382 |
+
2026-03-03 23:43:18.915283: Current learning rate: 0.00767
|
| 383 |
+
2026-03-03 23:45:49.950175: train_loss 0.5005
|
| 384 |
+
2026-03-03 23:45:50.051834: val_loss 0.497
|
| 385 |
+
2026-03-03 23:45:50.054470: Pseudo dice [np.float32(0.1088), np.float32(0.6779)]
|
| 386 |
+
2026-03-03 23:45:50.066610: Epoch time: 151.03 s
|
| 387 |
+
2026-03-03 23:45:50.066744: Yayy! New best EMA pseudo Dice: 0.3398999869823456
|
| 388 |
+
2026-03-03 23:45:56.952994:
|
| 389 |
+
2026-03-03 23:45:56.953173: Epoch 52
|
| 390 |
+
2026-03-03 23:45:56.953278: Current learning rate: 0.00763
|
| 391 |
+
2026-03-03 23:49:04.644494: train_loss 0.5029
|
| 392 |
+
2026-03-03 23:49:04.830956: val_loss 0.5218
|
| 393 |
+
2026-03-03 23:49:04.833451: Pseudo dice [np.float32(0.0911), np.float32(0.6291)]
|
| 394 |
+
2026-03-03 23:49:04.853979: Epoch time: 187.69 s
|
| 395 |
+
2026-03-03 23:49:04.854130: Yayy! New best EMA pseudo Dice: 0.3418999910354614
|
| 396 |
+
2026-03-03 23:49:12.193475:
|
| 397 |
+
2026-03-03 23:49:12.193701: Epoch 53
|
| 398 |
+
2026-03-03 23:49:12.193808: Current learning rate: 0.00758
|
| 399 |
+
2026-03-03 23:52:53.226912: train_loss 0.4821
|
| 400 |
+
2026-03-03 23:52:53.323367: val_loss 0.478
|
| 401 |
+
2026-03-03 23:52:53.325671: Pseudo dice [np.float32(0.0156), np.float32(0.714)]
|
| 402 |
+
2026-03-03 23:52:53.337856: Epoch time: 221.0 s
|
| 403 |
+
2026-03-03 23:52:53.338076: Yayy! New best EMA pseudo Dice: 0.3441999852657318
|
| 404 |
+
2026-03-03 23:53:26.668609:
|
| 405 |
+
2026-03-03 23:53:26.668770: Epoch 54
|
| 406 |
+
2026-03-03 23:53:26.668868: Current learning rate: 0.00753
|
| 407 |
+
2026-03-03 23:56:42.268045: train_loss 0.4957
|
| 408 |
+
2026-03-03 23:56:42.315224: val_loss 0.4932
|
| 409 |
+
2026-03-03 23:56:42.315436: Pseudo dice [np.float32(0.0906), np.float32(0.6528)]
|
| 410 |
+
2026-03-03 23:56:42.325384: Epoch time: 195.53 s
|
| 411 |
+
2026-03-03 23:56:42.325967: Yayy! New best EMA pseudo Dice: 0.34700000286102295
|
| 412 |
+
2026-03-03 23:56:50.780495:
|
| 413 |
+
2026-03-03 23:56:50.780672: Epoch 55
|
| 414 |
+
2026-03-03 23:56:50.780781: Current learning rate: 0.00749
|
| 415 |
+
2026-03-04 00:00:21.962127: train_loss 0.479
|
| 416 |
+
2026-03-04 00:00:22.089684: val_loss 0.5048
|
| 417 |
+
2026-03-04 00:00:22.091861: Pseudo dice [np.float32(0.1023), np.float32(0.6442)]
|
| 418 |
+
2026-03-04 00:00:22.098224: Epoch time: 211.18 s
|
| 419 |
+
2026-03-04 00:00:22.100893: Yayy! New best EMA pseudo Dice: 0.3495999872684479
|
| 420 |
+
2026-03-04 00:00:28.725967:
|
| 421 |
+
2026-03-04 00:00:28.726608: Epoch 56
|
| 422 |
+
2026-03-04 00:00:28.726719: Current learning rate: 0.00744
|
| 423 |
+
2026-03-04 00:04:16.357708: train_loss 0.494
|
| 424 |
+
2026-03-04 00:04:16.492266: val_loss 0.5168
|
| 425 |
+
2026-03-04 00:04:16.495605: Pseudo dice [np.float32(0.0248), np.float32(0.6788)]
|
| 426 |
+
2026-03-04 00:04:16.522729: Epoch time: 227.61 s
|
| 427 |
+
2026-03-04 00:04:16.522904: Yayy! New best EMA pseudo Dice: 0.3497999906539917
|
| 428 |
+
2026-03-04 00:04:24.215921:
|
| 429 |
+
2026-03-04 00:04:24.216180: Epoch 57
|
| 430 |
+
2026-03-04 00:04:24.216282: Current learning rate: 0.00739
|
| 431 |
+
2026-03-04 00:07:51.807353: train_loss 0.4662
|
| 432 |
+
2026-03-04 00:07:51.953877: val_loss 0.5357
|
| 433 |
+
2026-03-04 00:07:51.956845: Pseudo dice [np.float32(0.0463), np.float32(0.64)]
|
| 434 |
+
2026-03-04 00:07:51.971042: Epoch time: 207.59 s
|
| 435 |
+
2026-03-04 00:07:55.606047:
|
| 436 |
+
2026-03-04 00:07:55.606318: Epoch 58
|
| 437 |
+
2026-03-04 00:07:55.606420: Current learning rate: 0.00735
|
| 438 |
+
2026-03-04 00:11:34.052870: train_loss 0.507
|
| 439 |
+
2026-03-04 00:11:34.348627: val_loss 0.5277
|
| 440 |
+
2026-03-04 00:11:34.349930: Pseudo dice [np.float32(0.1266), np.float32(0.157)]
|
| 441 |
+
2026-03-04 00:11:34.361755: Epoch time: 218.34 s
|
| 442 |
+
2026-03-04 00:11:39.597329:
|
| 443 |
+
2026-03-04 00:11:39.597546: Epoch 59
|
| 444 |
+
2026-03-04 00:11:39.597646: Current learning rate: 0.0073
|
| 445 |
+
2026-03-04 00:15:09.685834: train_loss 0.4998
|
| 446 |
+
2026-03-04 00:15:09.912790: val_loss 0.4475
|
| 447 |
+
2026-03-04 00:15:09.913013: Pseudo dice [np.float32(0.1081), np.float32(0.7006)]
|
| 448 |
+
2026-03-04 00:15:09.938272: Epoch time: 210.09 s
|
| 449 |
+
2026-03-04 00:15:13.679866:
|
| 450 |
+
2026-03-04 00:15:13.680080: Epoch 60
|
| 451 |
+
2026-03-04 00:15:13.680206: Current learning rate: 0.00725
|
| 452 |
+
2026-03-04 00:18:57.723784: train_loss 0.5116
|
| 453 |
+
2026-03-04 00:18:58.018476: val_loss 0.5138
|
| 454 |
+
2026-03-04 00:18:58.024374: Pseudo dice [np.float32(0.1017), np.float32(0.5829)]
|
| 455 |
+
2026-03-04 00:18:58.073163: Epoch time: 223.93 s
|
| 456 |
+
2026-03-04 00:19:02.701795:
|
| 457 |
+
2026-03-04 00:19:02.701983: Epoch 61
|
| 458 |
+
2026-03-04 00:19:02.702086: Current learning rate: 0.00721
|
| 459 |
+
2026-03-04 00:22:33.862634: train_loss 0.5016
|
| 460 |
+
2026-03-04 00:22:34.057118: val_loss 0.5166
|
| 461 |
+
2026-03-04 00:22:34.066062: Pseudo dice [np.float32(0.0671), np.float32(0.6554)]
|
| 462 |
+
2026-03-04 00:22:34.084829: Epoch time: 211.16 s
|
| 463 |
+
2026-03-04 00:22:37.444630:
|
| 464 |
+
2026-03-04 00:22:37.444866: Epoch 62
|
| 465 |
+
2026-03-04 00:22:37.444967: Current learning rate: 0.00716
|
| 466 |
+
2026-03-04 00:26:18.622676: train_loss 0.5056
|
| 467 |
+
2026-03-04 00:26:18.822265: val_loss 0.5229
|
| 468 |
+
2026-03-04 00:26:18.822850: Pseudo dice [np.float32(0.1181), np.float32(0.6533)]
|
| 469 |
+
2026-03-04 00:26:18.827272: Epoch time: 221.11 s
|
| 470 |
+
2026-03-04 00:26:22.763644:
|
| 471 |
+
2026-03-04 00:26:22.763839: Epoch 63
|
| 472 |
+
2026-03-04 00:26:22.763942: Current learning rate: 0.00711
|
| 473 |
+
2026-03-04 00:29:50.455787: train_loss 0.5064
|
| 474 |
+
2026-03-04 00:29:50.656666: val_loss 0.5247
|
| 475 |
+
2026-03-04 00:29:50.661130: Pseudo dice [np.float32(0.0531), np.float32(0.5406)]
|
| 476 |
+
2026-03-04 00:29:50.678627: Epoch time: 207.67 s
|
| 477 |
+
2026-03-04 00:29:54.307804:
|
| 478 |
+
2026-03-04 00:29:54.309595: Epoch 64
|
| 479 |
+
2026-03-04 00:29:54.309701: Current learning rate: 0.00707
|
| 480 |
+
2026-03-04 00:33:31.881483: train_loss 0.5012
|
| 481 |
+
2026-03-04 00:33:32.064355: val_loss 0.5488
|
| 482 |
+
2026-03-04 00:33:32.067686: Pseudo dice [np.float32(0.0704), np.float32(0.613)]
|
| 483 |
+
2026-03-04 00:33:32.094716: Epoch time: 217.55 s
|
| 484 |
+
2026-03-04 00:33:39.232162:
|
| 485 |
+
2026-03-04 00:33:39.232290: Epoch 65
|
| 486 |
+
2026-03-04 00:33:39.232387: Current learning rate: 0.00702
|
| 487 |
+
2026-03-04 00:37:00.817702: train_loss 0.4985
|
| 488 |
+
2026-03-04 00:37:00.944354: val_loss 0.5043
|
| 489 |
+
2026-03-04 00:37:00.944502: Pseudo dice [np.float32(0.0904), np.float32(0.6921)]
|
| 490 |
+
2026-03-04 00:37:00.971168: Epoch time: 201.58 s
|
| 491 |
+
2026-03-04 00:37:04.159027:
|
| 492 |
+
2026-03-04 00:37:04.159285: Epoch 66
|
| 493 |
+
2026-03-04 00:37:04.159405: Current learning rate: 0.00697
|
| 494 |
+
2026-03-04 00:40:48.889773: train_loss 0.4821
|
| 495 |
+
2026-03-04 00:40:48.995478: val_loss 0.4909
|
| 496 |
+
2026-03-04 00:40:48.996569: Pseudo dice [np.float32(0.0994), np.float32(0.6737)]
|
| 497 |
+
2026-03-04 00:40:49.023421: Epoch time: 224.72 s
|
| 498 |
+
2026-03-04 00:40:53.376849:
|
| 499 |
+
2026-03-04 00:40:53.377079: Epoch 67
|
| 500 |
+
2026-03-04 00:40:53.377204: Current learning rate: 0.00693
|
| 501 |
+
2026-03-04 00:44:22.938040: train_loss 0.4958
|
| 502 |
+
2026-03-04 00:44:23.147716: val_loss 0.5081
|
| 503 |
+
2026-03-04 00:44:23.150680: Pseudo dice [np.float32(0.0503), np.float32(0.663)]
|
| 504 |
+
2026-03-04 00:44:23.172688: Epoch time: 209.57 s
|
| 505 |
+
2026-03-04 00:44:52.954376:
|
| 506 |
+
2026-03-04 00:44:52.954568: Epoch 68
|
| 507 |
+
2026-03-04 00:44:52.954668: Current learning rate: 0.00688
|
| 508 |
+
2026-03-04 00:48:23.198936: train_loss 0.4824
|
| 509 |
+
2026-03-04 00:48:23.352030: val_loss 0.4851
|
| 510 |
+
2026-03-04 00:48:23.353611: Pseudo dice [np.float32(0.1297), np.float32(0.665)]
|
| 511 |
+
2026-03-04 00:48:23.381060: Epoch time: 210.16 s
|
| 512 |
+
2026-03-04 00:48:23.381696: Yayy! New best EMA pseudo Dice: 0.35429999232292175
|
| 513 |
+
2026-03-04 00:48:31.922709:
|
| 514 |
+
2026-03-04 00:48:31.922853: Epoch 69
|
| 515 |
+
2026-03-04 00:48:31.922952: Current learning rate: 0.00683
|
| 516 |
+
2026-03-04 00:52:08.022194: train_loss 0.5343
|
| 517 |
+
2026-03-04 00:52:08.187812: val_loss 0.5148
|
| 518 |
+
2026-03-04 00:52:08.190642: Pseudo dice [np.float32(0.001), np.float32(0.6971)]
|
| 519 |
+
2026-03-04 00:52:08.202480: Epoch time: 216.1 s
|
| 520 |
+
2026-03-04 00:52:11.778786:
|
| 521 |
+
2026-03-04 00:52:11.782122: Epoch 70
|
| 522 |
+
2026-03-04 00:52:11.782366: Current learning rate: 0.00679
|
| 523 |
+
2026-03-04 00:55:47.041415: train_loss 0.5094
|
| 524 |
+
2026-03-04 00:55:47.323189: val_loss 0.4948
|
| 525 |
+
2026-03-04 00:55:47.325487: Pseudo dice [np.float32(0.007), np.float32(0.688)]
|
| 526 |
+
2026-03-04 00:55:47.372938: Epoch time: 215.21 s
|
| 527 |
+
2026-03-04 00:55:51.853747:
|
| 528 |
+
2026-03-04 00:55:51.858344: Epoch 71
|
| 529 |
+
2026-03-04 00:55:51.858488: Current learning rate: 0.00674
|
| 530 |
+
2026-03-04 00:59:26.622360: train_loss 0.5225
|
| 531 |
+
2026-03-04 00:59:26.852830: val_loss 0.5243
|
| 532 |
+
2026-03-04 00:59:26.855110: Pseudo dice [np.float32(0.1555), np.float32(0.0846)]
|
| 533 |
+
2026-03-04 00:59:26.884475: Epoch time: 214.74 s
|
| 534 |
+
2026-03-04 00:59:31.592406:
|
| 535 |
+
2026-03-04 00:59:31.595363: Epoch 72
|
| 536 |
+
2026-03-04 00:59:31.595872: Current learning rate: 0.00669
|
| 537 |
+
2026-03-04 01:03:13.707249: train_loss 0.5119
|
| 538 |
+
2026-03-04 01:03:13.954990: val_loss 0.463
|
| 539 |
+
2026-03-04 01:03:13.959826: Pseudo dice [np.float32(0.1386), np.float32(0.6885)]
|
| 540 |
+
2026-03-04 01:03:13.989385: Epoch time: 222.06 s
|
| 541 |
+
2026-03-04 01:03:18.375522:
|
| 542 |
+
2026-03-04 01:03:18.379607: Epoch 73
|
| 543 |
+
2026-03-04 01:03:18.379717: Current learning rate: 0.00665
|
| 544 |
+
2026-03-04 01:06:56.567352: train_loss 0.503
|
| 545 |
+
2026-03-04 01:06:56.657974: val_loss 0.5424
|
| 546 |
+
2026-03-04 01:06:56.664128: Pseudo dice [np.float32(0.0836), np.float32(0.6174)]
|
| 547 |
+
2026-03-04 01:06:56.673654: Epoch time: 218.19 s
|
| 548 |
+
2026-03-04 01:07:01.708200:
|
| 549 |
+
2026-03-04 01:07:01.708456: Epoch 74
|
| 550 |
+
2026-03-04 01:07:01.708558: Current learning rate: 0.0066
|
| 551 |
+
2026-03-04 01:10:39.098756: train_loss 0.5019
|
| 552 |
+
2026-03-04 01:10:39.210104: val_loss 0.4618
|
| 553 |
+
2026-03-04 01:10:39.213978: Pseudo dice [np.float32(0.099), np.float32(0.699)]
|
| 554 |
+
2026-03-04 01:10:39.232117: Epoch time: 217.34 s
|
| 555 |
+
2026-03-04 01:10:44.128469:
|
| 556 |
+
2026-03-04 01:10:44.129275: Epoch 75
|
| 557 |
+
2026-03-04 01:10:44.129376: Current learning rate: 0.00655
|
| 558 |
+
2026-03-04 01:14:14.447480: train_loss 0.5015
|
| 559 |
+
2026-03-04 01:14:14.630094: val_loss 0.5535
|
| 560 |
+
2026-03-04 01:14:14.634099: Pseudo dice [np.float32(0.1159), np.float32(0.114)]
|
| 561 |
+
2026-03-04 01:14:14.655420: Epoch time: 210.32 s
|
| 562 |
+
2026-03-04 01:14:18.354402:
|
| 563 |
+
2026-03-04 01:14:18.354663: Epoch 76
|
| 564 |
+
2026-03-04 01:14:18.354765: Current learning rate: 0.0065
|
| 565 |
+
2026-03-04 01:18:06.100676: train_loss 0.4864
|
| 566 |
+
2026-03-04 01:18:06.291800: val_loss 0.4896
|
| 567 |
+
2026-03-04 01:18:06.291956: Pseudo dice [np.float32(0.1638), np.float32(0.6744)]
|
| 568 |
+
2026-03-04 01:18:06.319553: Epoch time: 227.71 s
|
| 569 |
+
2026-03-04 01:18:10.859829:
|
| 570 |
+
2026-03-04 01:18:10.860038: Epoch 77
|
| 571 |
+
2026-03-04 01:18:10.860758: Current learning rate: 0.00646
|
| 572 |
+
2026-03-04 01:21:47.740511: train_loss 0.4915
|
| 573 |
+
2026-03-04 01:21:48.014236: val_loss 0.4849
|
| 574 |
+
2026-03-04 01:21:48.014411: Pseudo dice [np.float32(0.1407), np.float32(0.6755)]
|
| 575 |
+
2026-03-04 01:21:48.045099: Epoch time: 216.83 s
|
| 576 |
+
2026-03-04 01:21:52.300019:
|
| 577 |
+
2026-03-04 01:21:52.300735: Epoch 78
|
| 578 |
+
2026-03-04 01:21:52.300837: Current learning rate: 0.00641
|
| 579 |
+
2026-03-04 01:25:16.338696: train_loss 0.4969
|
| 580 |
+
2026-03-04 01:25:16.416266: val_loss 0.4743
|
| 581 |
+
2026-03-04 01:25:16.416540: Pseudo dice [np.float32(0.1737), np.float32(0.5992)]
|
| 582 |
+
2026-03-04 01:25:16.426373: Epoch time: 204.04 s
|
| 583 |
+
2026-03-04 01:25:19.656727:
|
| 584 |
+
2026-03-04 01:25:19.656830: Epoch 79
|
| 585 |
+
2026-03-04 01:25:19.656936: Current learning rate: 0.00636
|
| 586 |
+
2026-03-04 01:29:03.263321: train_loss 0.4849
|
| 587 |
+
2026-03-04 01:29:03.333760: val_loss 0.5169
|
| 588 |
+
2026-03-04 01:29:03.333905: Pseudo dice [np.float32(0.0894), np.float32(0.6364)]
|
| 589 |
+
2026-03-04 01:29:03.337742: Epoch time: 223.58 s
|
| 590 |
+
2026-03-04 01:29:07.931537:
|
| 591 |
+
2026-03-04 01:29:07.931764: Epoch 80
|
| 592 |
+
2026-03-04 01:29:07.931866: Current learning rate: 0.00631
|
| 593 |
+
2026-03-04 01:32:41.686824: train_loss 0.5004
|
| 594 |
+
2026-03-04 01:32:42.002423: val_loss 0.4721
|
| 595 |
+
2026-03-04 01:32:42.004968: Pseudo dice [np.float32(0.169), np.float32(0.6745)]
|
| 596 |
+
2026-03-04 01:32:42.050113: Epoch time: 213.75 s
|
| 597 |
+
2026-03-04 01:32:46.295163:
|
| 598 |
+
2026-03-04 01:32:46.295418: Epoch 81
|
| 599 |
+
2026-03-04 01:32:46.295515: Current learning rate: 0.00627
|
| 600 |
+
2026-03-04 01:36:20.912018: train_loss 0.5041
|
| 601 |
+
2026-03-04 01:36:20.940797: val_loss 0.4931
|
| 602 |
+
2026-03-04 01:36:20.941264: Pseudo dice [np.float32(0.1713), np.float32(0.6362)]
|
| 603 |
+
2026-03-04 01:36:20.946183: Epoch time: 214.6 s
|
| 604 |
+
2026-03-04 01:36:20.947002: Yayy! New best EMA pseudo Dice: 0.3587000072002411
|
| 605 |
+
2026-03-04 01:36:55.741081:
|
| 606 |
+
2026-03-04 01:36:55.741265: Epoch 82
|
| 607 |
+
2026-03-04 01:36:55.741378: Current learning rate: 0.00622
|
| 608 |
+
2026-03-04 01:40:11.162661: train_loss 0.4844
|
| 609 |
+
2026-03-04 01:40:11.269581: val_loss 0.4568
|
| 610 |
+
2026-03-04 01:40:11.269897: Pseudo dice [np.float32(0.1069), np.float32(0.6649)]
|
| 611 |
+
2026-03-04 01:40:11.314379: Epoch time: 195.42 s
|
| 612 |
+
2026-03-04 01:40:11.314557: Yayy! New best EMA pseudo Dice: 0.36149999499320984
|
| 613 |
+
2026-03-04 01:40:19.796307:
|
| 614 |
+
2026-03-04 01:40:19.796576: Epoch 83
|
| 615 |
+
2026-03-04 01:40:19.796676: Current learning rate: 0.00617
|
| 616 |
+
2026-03-04 01:44:01.197431: train_loss 0.487
|
| 617 |
+
2026-03-04 01:44:01.511857: val_loss 0.5209
|
| 618 |
+
2026-03-04 01:44:01.512065: Pseudo dice [np.float32(0.0166), np.float32(0.6579)]
|
| 619 |
+
2026-03-04 01:44:01.562387: Epoch time: 221.38 s
|
| 620 |
+
2026-03-04 01:44:05.857500:
|
| 621 |
+
2026-03-04 01:44:05.875018: Epoch 84
|
| 622 |
+
2026-03-04 01:44:05.875128: Current learning rate: 0.00612
|
| 623 |
+
2026-03-04 01:47:27.988832: train_loss 0.5025
|
| 624 |
+
2026-03-04 01:47:28.135697: val_loss 0.5164
|
| 625 |
+
2026-03-04 01:47:28.136061: Pseudo dice [np.float32(0.134), np.float32(0.6573)]
|
| 626 |
+
2026-03-04 01:47:28.142834: Epoch time: 202.13 s
|
| 627 |
+
2026-03-04 01:47:28.143779: Yayy! New best EMA pseudo Dice: 0.3626999855041504
|
| 628 |
+
2026-03-04 01:47:36.136287:
|
| 629 |
+
2026-03-04 01:47:36.136553: Epoch 85
|
| 630 |
+
2026-03-04 01:47:36.136654: Current learning rate: 0.00608
|
| 631 |
+
2026-03-04 01:51:14.345525: train_loss 0.4867
|
| 632 |
+
2026-03-04 01:51:14.364338: val_loss 0.4554
|
| 633 |
+
2026-03-04 01:51:14.364571: Pseudo dice [np.float32(0.1453), np.float32(0.693)]
|
| 634 |
+
2026-03-04 01:51:14.368319: Epoch time: 218.2 s
|
| 635 |
+
2026-03-04 01:51:14.368894: Yayy! New best EMA pseudo Dice: 0.3682999908924103
|
| 636 |
+
2026-03-04 01:51:22.167543:
|
| 637 |
+
2026-03-04 01:51:22.167810: Epoch 86
|
| 638 |
+
2026-03-04 01:51:22.167911: Current learning rate: 0.00603
|
| 639 |
+
2026-03-04 01:54:50.196065: train_loss 0.4832
|
| 640 |
+
2026-03-04 01:54:50.430698: val_loss 0.4971
|
| 641 |
+
2026-03-04 01:54:50.432951: Pseudo dice [np.float32(0.1714), np.float32(0.6518)]
|
| 642 |
+
2026-03-04 01:54:50.462125: Epoch time: 207.96 s
|
| 643 |
+
2026-03-04 01:54:50.462306: Yayy! New best EMA pseudo Dice: 0.3727000057697296
|
| 644 |
+
2026-03-04 01:54:58.131991:
|
| 645 |
+
2026-03-04 01:54:58.132197: Epoch 87
|
| 646 |
+
2026-03-04 01:54:58.132297: Current learning rate: 0.00598
|
| 647 |
+
2026-03-04 01:58:38.514814: train_loss 0.4863
|
| 648 |
+
2026-03-04 01:58:38.812078: val_loss 0.5105
|
| 649 |
+
2026-03-04 01:58:38.814504: Pseudo dice [np.float32(0.189), np.float32(0.6237)]
|
| 650 |
+
2026-03-04 01:58:38.852482: Epoch time: 220.34 s
|
| 651 |
+
2026-03-04 01:58:38.852680: Yayy! New best EMA pseudo Dice: 0.37599998712539673
|
| 652 |
+
2026-03-04 01:58:47.911445:
|
| 653 |
+
2026-03-04 01:58:47.911644: Epoch 88
|
| 654 |
+
2026-03-04 01:58:47.911742: Current learning rate: 0.00593
|
| 655 |
+
2026-03-04 02:02:21.249912: train_loss 0.479
|
| 656 |
+
2026-03-04 02:02:21.386495: val_loss 0.4941
|
| 657 |
+
2026-03-04 02:02:21.386675: Pseudo dice [np.float32(0.1944), np.float32(0.6622)]
|
| 658 |
+
2026-03-04 02:02:21.407386: Epoch time: 213.34 s
|
| 659 |
+
2026-03-04 02:02:21.410833: Yayy! New best EMA pseudo Dice: 0.3813000023365021
|
| 660 |
+
2026-03-04 02:02:30.503765:
|
| 661 |
+
2026-03-04 02:02:30.503929: Epoch 89
|
| 662 |
+
2026-03-04 02:02:30.504038: Current learning rate: 0.00589
|
| 663 |
+
2026-03-04 02:06:14.919684: train_loss 0.4698
|
| 664 |
+
2026-03-04 02:06:15.139430: val_loss 0.5035
|
| 665 |
+
2026-03-04 02:06:15.139740: Pseudo dice [np.float32(0.1931), np.float32(0.5924)]
|
| 666 |
+
2026-03-04 02:06:15.196812: Epoch time: 224.34 s
|
| 667 |
+
2026-03-04 02:06:15.207324: Yayy! New best EMA pseudo Dice: 0.3824000060558319
|
| 668 |
+
2026-03-04 02:06:22.952461:
|
| 669 |
+
2026-03-04 02:06:22.953601: Epoch 90
|
| 670 |
+
2026-03-04 02:06:22.953707: Current learning rate: 0.00584
|
| 671 |
+
2026-03-04 02:09:51.826789: train_loss 0.4944
|
| 672 |
+
2026-03-04 02:09:51.991358: val_loss 0.4778
|
| 673 |
+
2026-03-04 02:09:51.991621: Pseudo dice [np.float32(0.1533), np.float32(0.6845)]
|
| 674 |
+
2026-03-04 02:09:52.014156: Epoch time: 208.88 s
|
| 675 |
+
2026-03-04 02:09:52.014402: Yayy! New best EMA pseudo Dice: 0.38600000739097595
|
| 676 |
+
2026-03-04 02:09:58.603322:
|
| 677 |
+
2026-03-04 02:09:58.603511: Epoch 91
|
| 678 |
+
2026-03-04 02:09:58.603610: Current learning rate: 0.00579
|
| 679 |
+
2026-03-04 02:13:42.150170: train_loss 0.4752
|
| 680 |
+
2026-03-04 02:13:42.372884: val_loss 0.4634
|
| 681 |
+
2026-03-04 02:13:42.373061: Pseudo dice [np.float32(0.176), np.float32(0.6805)]
|
| 682 |
+
2026-03-04 02:13:42.399186: Epoch time: 223.5 s
|
| 683 |
+
2026-03-04 02:13:42.399351: Yayy! New best EMA pseudo Dice: 0.3903000056743622
|
| 684 |
+
2026-03-04 02:13:51.097170:
|
| 685 |
+
2026-03-04 02:13:51.097280: Epoch 92
|
| 686 |
+
2026-03-04 02:13:51.097374: Current learning rate: 0.00574
|
| 687 |
+
2026-03-04 02:17:22.759543: train_loss 0.4671
|
| 688 |
+
2026-03-04 02:17:22.913719: val_loss 0.5235
|
| 689 |
+
2026-03-04 02:17:22.914103: Pseudo dice [np.float32(0.1891), np.float32(0.6508)]
|
| 690 |
+
2026-03-04 02:17:22.921012: Epoch time: 211.66 s
|
| 691 |
+
2026-03-04 02:17:22.921598: Yayy! New best EMA pseudo Dice: 0.39320001006126404
|
| 692 |
+
2026-03-04 02:17:30.347312:
|
| 693 |
+
2026-03-04 02:17:30.347554: Epoch 93
|
| 694 |
+
2026-03-04 02:17:30.347655: Current learning rate: 0.0057
|
| 695 |
+
2026-03-04 02:21:18.796276: train_loss 0.4851
|
| 696 |
+
2026-03-04 02:21:18.893100: val_loss 0.444
|
| 697 |
+
2026-03-04 02:21:18.893356: Pseudo dice [np.float32(0.201), np.float32(0.6942)]
|
| 698 |
+
2026-03-04 02:21:18.908413: Epoch time: 228.44 s
|
| 699 |
+
2026-03-04 02:21:18.908742: Yayy! New best EMA pseudo Dice: 0.3986999988555908
|
| 700 |
+
2026-03-04 02:21:27.227622:
|
| 701 |
+
2026-03-04 02:21:27.228326: Epoch 94
|
| 702 |
+
2026-03-04 02:21:27.228427: Current learning rate: 0.00565
|
| 703 |
+
2026-03-04 02:24:52.960377: train_loss 0.4994
|
| 704 |
+
2026-03-04 02:24:53.080217: val_loss 0.5089
|
| 705 |
+
2026-03-04 02:24:53.080573: Pseudo dice [np.float32(0.1587), np.float32(0.6097)]
|
| 706 |
+
2026-03-04 02:24:53.097529: Epoch time: 205.73 s
|
| 707 |
+
2026-03-04 02:24:57.413506:
|
| 708 |
+
2026-03-04 02:24:57.413695: Epoch 95
|
| 709 |
+
2026-03-04 02:24:57.413795: Current learning rate: 0.0056
|
| 710 |
+
2026-03-04 02:28:34.939451: train_loss 0.4814
|
| 711 |
+
2026-03-04 02:28:35.122070: val_loss 0.4437
|
| 712 |
+
2026-03-04 02:28:35.122441: Pseudo dice [np.float32(0.1802), np.float32(0.6932)]
|
| 713 |
+
2026-03-04 02:28:35.157103: Epoch time: 217.52 s
|
| 714 |
+
2026-03-04 02:28:35.160740: Yayy! New best EMA pseudo Dice: 0.40119999647140503
|
| 715 |
+
2026-03-04 02:28:42.967993:
|
| 716 |
+
2026-03-04 02:28:42.968260: Epoch 96
|
| 717 |
+
2026-03-04 02:28:42.968359: Current learning rate: 0.00555
|
| 718 |
+
2026-03-04 02:32:21.701038: train_loss 0.4858
|
| 719 |
+
2026-03-04 02:32:21.866068: val_loss 0.4665
|
| 720 |
+
2026-03-04 02:32:21.866221: Pseudo dice [np.float32(0.196), np.float32(0.6692)]
|
| 721 |
+
2026-03-04 02:32:21.890286: Epoch time: 218.71 s
|
| 722 |
+
2026-03-04 02:32:21.890548: Yayy! New best EMA pseudo Dice: 0.4043000042438507
|
| 723 |
+
2026-03-04 02:32:29.793943:
|
| 724 |
+
2026-03-04 02:32:29.794247: Epoch 97
|
| 725 |
+
2026-03-04 02:32:29.794381: Current learning rate: 0.0055
|
| 726 |
+
2026-03-04 02:36:05.572910: train_loss 0.4792
|
| 727 |
+
2026-03-04 02:36:05.753467: val_loss 0.4795
|
| 728 |
+
2026-03-04 02:36:05.753605: Pseudo dice [np.float32(0.2022), np.float32(0.6312)]
|
| 729 |
+
2026-03-04 02:36:05.787615: Epoch time: 215.75 s
|
| 730 |
+
2026-03-04 02:36:05.787783: Yayy! New best EMA pseudo Dice: 0.40560001134872437
|
| 731 |
+
2026-03-04 02:36:42.416191:
|
| 732 |
+
2026-03-04 02:36:42.416475: Epoch 98
|
| 733 |
+
2026-03-04 02:36:42.416577: Current learning rate: 0.00546
|
| 734 |
+
2026-03-04 02:39:55.568962: train_loss 0.4812
|
| 735 |
+
2026-03-04 02:39:55.826475: val_loss 0.4136
|
| 736 |
+
2026-03-04 02:39:55.826629: Pseudo dice [np.float32(0.0901), np.float32(0.7342)]
|
| 737 |
+
2026-03-04 02:39:55.865544: Epoch time: 193.1 s
|
| 738 |
+
2026-03-04 02:39:55.865690: Yayy! New best EMA pseudo Dice: 0.40619999170303345
|
| 739 |
+
2026-03-04 02:40:03.339853:
|
| 740 |
+
2026-03-04 02:40:03.340068: Epoch 99
|
| 741 |
+
2026-03-04 02:40:03.340198: Current learning rate: 0.00541
|
| 742 |
+
2026-03-04 02:43:38.102817: train_loss 0.4933
|
| 743 |
+
2026-03-04 02:43:38.300385: val_loss 0.521
|
| 744 |
+
2026-03-04 02:43:38.302405: Pseudo dice [np.float32(0.1732), np.float32(0.6108)]
|
| 745 |
+
2026-03-04 02:43:38.325985: Epoch time: 214.72 s
|
| 746 |
+
2026-03-04 02:43:47.312979:
|
| 747 |
+
2026-03-04 02:43:47.313205: Epoch 100
|
| 748 |
+
2026-03-04 02:43:47.313313: Current learning rate: 0.00536
|
| 749 |
+
2026-03-04 02:47:10.400639: train_loss 0.5079
|
| 750 |
+
2026-03-04 02:47:10.524838: val_loss 0.4771
|
| 751 |
+
2026-03-04 02:47:10.527380: Pseudo dice [np.float32(0.1546), np.float32(0.6709)]
|
| 752 |
+
2026-03-04 02:47:10.541649: Epoch time: 203.09 s
|
| 753 |
+
2026-03-04 02:47:13.967440:
|
| 754 |
+
2026-03-04 02:47:13.968066: Epoch 101
|
| 755 |
+
2026-03-04 02:47:13.968204: Current learning rate: 0.00531
|
| 756 |
+
2026-03-04 02:50:57.446077: train_loss 0.4789
|
| 757 |
+
2026-03-04 02:50:57.634181: val_loss 0.4703
|
| 758 |
+
2026-03-04 02:50:57.637217: Pseudo dice [np.float32(0.0512), np.float32(0.6756)]
|
| 759 |
+
2026-03-04 02:50:57.656280: Epoch time: 223.48 s
|
| 760 |
+
2026-03-04 02:51:01.734971:
|
| 761 |
+
2026-03-04 02:51:01.737077: Epoch 102
|
| 762 |
+
2026-03-04 02:51:01.737584: Current learning rate: 0.00526
|
| 763 |
+
2026-03-04 02:54:53.541623: train_loss 0.4717
|
| 764 |
+
2026-03-04 02:54:53.704404: val_loss 0.566
|
| 765 |
+
2026-03-04 02:54:53.711087: Pseudo dice [np.float32(0.1819), np.float32(0.5982)]
|
| 766 |
+
2026-03-04 02:54:53.727691: Epoch time: 231.73 s
|
| 767 |
+
2026-03-04 02:54:58.017906:
|
| 768 |
+
2026-03-04 02:54:58.027374: Epoch 103
|
| 769 |
+
2026-03-04 02:54:58.028093: Current learning rate: 0.00521
|
| 770 |
+
2026-03-04 02:58:26.073666: train_loss 0.4798
|
| 771 |
+
2026-03-04 02:58:26.238651: val_loss 0.448
|
| 772 |
+
2026-03-04 02:58:26.243212: Pseudo dice [np.float32(0.2088), np.float32(0.5145)]
|
| 773 |
+
2026-03-04 02:58:26.259482: Epoch time: 208.06 s
|
| 774 |
+
2026-03-04 02:58:30.688638:
|
| 775 |
+
2026-03-04 02:58:30.688887: Epoch 104
|
| 776 |
+
2026-03-04 02:58:30.688987: Current learning rate: 0.00517
|
| 777 |
+
2026-03-04 03:02:11.847168: train_loss 0.4735
|
| 778 |
+
2026-03-04 03:02:11.900334: val_loss 0.4663
|
| 779 |
+
2026-03-04 03:02:11.901176: Pseudo dice [np.float32(0.2095), np.float32(0.6513)]
|
| 780 |
+
2026-03-04 03:02:11.911981: Epoch time: 221.15 s
|
| 781 |
+
2026-03-04 03:02:16.838625:
|
| 782 |
+
2026-03-04 03:02:16.843654: Epoch 105
|
| 783 |
+
2026-03-04 03:02:16.843761: Current learning rate: 0.00512
|
| 784 |
+
2026-03-04 03:05:55.000760: train_loss 0.4617
|
| 785 |
+
2026-03-04 03:05:55.095916: val_loss 0.4306
|
| 786 |
+
2026-03-04 03:05:55.618562: Pseudo dice [np.float32(0.2174), np.float32(0.6861)]
|
| 787 |
+
2026-03-04 03:05:55.626164: Epoch time: 218.16 s
|
| 788 |
+
2026-03-04 03:05:58.811035:
|
| 789 |
+
2026-03-04 03:05:58.811278: Epoch 106
|
| 790 |
+
2026-03-04 03:05:58.811379: Current learning rate: 0.00507
|
| 791 |
+
2026-03-04 03:09:38.480979: train_loss 0.4682
|
| 792 |
+
2026-03-04 03:09:38.608394: val_loss 0.447
|
| 793 |
+
2026-03-04 03:09:38.609243: Pseudo dice [np.float32(0.2186), np.float32(0.6808)]
|
| 794 |
+
2026-03-04 03:09:38.613216: Epoch time: 219.62 s
|
| 795 |
+
2026-03-04 03:09:38.614668: Yayy! New best EMA pseudo Dice: 0.40950000286102295
|
| 796 |
+
2026-03-04 03:09:47.702302:
|
| 797 |
+
2026-03-04 03:09:47.702475: Epoch 107
|
| 798 |
+
2026-03-04 03:09:47.702574: Current learning rate: 0.00502
|
| 799 |
+
2026-03-04 03:13:21.243217: train_loss 0.4796
|
| 800 |
+
2026-03-04 03:13:21.422390: val_loss 0.4145
|
| 801 |
+
2026-03-04 03:13:21.428421: Pseudo dice [np.float32(0.1926), np.float32(0.716)]
|
| 802 |
+
2026-03-04 03:13:21.461378: Epoch time: 213.54 s
|
| 803 |
+
2026-03-04 03:13:21.473897: Yayy! New best EMA pseudo Dice: 0.4138999879360199
|
| 804 |
+
2026-03-04 03:13:28.548195:
|
| 805 |
+
2026-03-04 03:13:28.548476: Epoch 108
|
| 806 |
+
2026-03-04 03:13:28.548576: Current learning rate: 0.00497
|
| 807 |
+
2026-03-04 03:17:13.323718: train_loss 0.4788
|
| 808 |
+
2026-03-04 03:17:13.439601: val_loss 0.4823
|
| 809 |
+
2026-03-04 03:17:13.440287: Pseudo dice [np.float32(0.2073), np.float32(0.4874)]
|
| 810 |
+
2026-03-04 03:17:13.444913: Epoch time: 224.73 s
|
| 811 |
+
2026-03-04 03:17:18.259365:
|
| 812 |
+
2026-03-04 03:17:18.259552: Epoch 109
|
| 813 |
+
2026-03-04 03:17:18.259653: Current learning rate: 0.00492
|
| 814 |
+
2026-03-04 03:20:45.328838: train_loss 0.4614
|
| 815 |
+
2026-03-04 03:20:45.443799: val_loss 0.4498
|
| 816 |
+
2026-03-04 03:20:45.444088: Pseudo dice [np.float32(0.1813), np.float32(0.6994)]
|
| 817 |
+
2026-03-04 03:20:45.461724: Epoch time: 207.07 s
|
| 818 |
+
2026-03-04 03:20:49.621652:
|
| 819 |
+
2026-03-04 03:20:49.621889: Epoch 110
|
| 820 |
+
2026-03-04 03:20:49.621988: Current learning rate: 0.00487
|
| 821 |
+
2026-03-04 03:24:31.719275: train_loss 0.4609
|
| 822 |
+
2026-03-04 03:24:31.912743: val_loss 0.4682
|
| 823 |
+
2026-03-04 03:24:31.918827: Pseudo dice [np.float32(0.1945), np.float32(0.6645)]
|
| 824 |
+
2026-03-04 03:24:31.958411: Epoch time: 222.02 s
|
| 825 |
+
2026-03-04 03:24:36.165702:
|
| 826 |
+
2026-03-04 03:24:36.165947: Epoch 111
|
| 827 |
+
2026-03-04 03:24:36.166049: Current learning rate: 0.00483
|
| 828 |
+
2026-03-04 03:28:09.497198: train_loss 0.4796
|
| 829 |
+
2026-03-04 03:28:09.639571: val_loss 0.4781
|
| 830 |
+
2026-03-04 03:28:09.647323: Pseudo dice [np.float32(0.1962), np.float32(0.649)]
|
| 831 |
+
2026-03-04 03:28:09.695525: Epoch time: 213.32 s
|
| 832 |
+
2026-03-04 03:28:13.557652:
|
| 833 |
+
2026-03-04 03:28:13.557825: Epoch 112
|
| 834 |
+
2026-03-04 03:28:13.557925: Current learning rate: 0.00478
|
| 835 |
+
2026-03-04 03:31:51.375559: train_loss 0.4518
|
| 836 |
+
2026-03-04 03:31:51.617864: val_loss 0.5111
|
| 837 |
+
2026-03-04 03:31:51.623852: Pseudo dice [np.float32(0.1777), np.float32(0.648)]
|
| 838 |
+
2026-03-04 03:31:51.669856: Epoch time: 217.74 s
|
| 839 |
+
2026-03-04 03:32:24.327318:
|
| 840 |
+
2026-03-04 03:32:24.327608: Epoch 113
|
| 841 |
+
2026-03-04 03:32:24.327723: Current learning rate: 0.00473
|
| 842 |
+
2026-03-04 03:35:36.577363: train_loss 0.5094
|
| 843 |
+
2026-03-04 03:35:36.826005: val_loss 0.5163
|
| 844 |
+
2026-03-04 03:35:36.827452: Pseudo dice [np.float32(0.2627), np.float32(0.4992)]
|
| 845 |
+
2026-03-04 03:35:36.856525: Epoch time: 192.19 s
|
| 846 |
+
2026-03-04 03:35:41.371020:
|
| 847 |
+
2026-03-04 03:35:41.371335: Epoch 114
|
| 848 |
+
2026-03-04 03:35:41.371465: Current learning rate: 0.00468
|
| 849 |
+
2026-03-04 03:39:17.632606: train_loss 0.4572
|
| 850 |
+
2026-03-04 03:39:17.797871: val_loss 0.4872
|
| 851 |
+
2026-03-04 03:39:17.798403: Pseudo dice [np.float32(0.1721), np.float32(0.6655)]
|
| 852 |
+
2026-03-04 03:39:17.803235: Epoch time: 216.23 s
|
| 853 |
+
2026-03-04 03:39:23.144079:
|
| 854 |
+
2026-03-04 03:39:23.159420: Epoch 115
|
| 855 |
+
2026-03-04 03:39:23.159525: Current learning rate: 0.00463
|
| 856 |
+
2026-03-04 03:42:57.229648: train_loss 0.4685
|
| 857 |
+
2026-03-04 03:42:57.351420: val_loss 0.4676
|
| 858 |
+
2026-03-04 03:42:57.354773: Pseudo dice [np.float32(0.2364), np.float32(0.5821)]
|
| 859 |
+
2026-03-04 03:42:57.377019: Epoch time: 214.09 s
|
| 860 |
+
2026-03-04 03:43:01.502943:
|
| 861 |
+
2026-03-04 03:43:01.503228: Epoch 116
|
| 862 |
+
2026-03-04 03:43:01.503339: Current learning rate: 0.00458
|
| 863 |
+
2026-03-04 03:46:46.199280: train_loss 0.4627
|
| 864 |
+
2026-03-04 03:46:46.222366: val_loss 0.4833
|
| 865 |
+
2026-03-04 03:46:46.222831: Pseudo dice [np.float32(0.179), np.float32(0.6406)]
|
| 866 |
+
2026-03-04 03:46:46.227976: Epoch time: 224.64 s
|
| 867 |
+
2026-03-04 03:46:50.628609:
|
| 868 |
+
2026-03-04 03:46:50.629342: Epoch 117
|
| 869 |
+
2026-03-04 03:46:50.629488: Current learning rate: 0.00453
|
| 870 |
+
2026-03-04 03:50:19.573631: train_loss 0.4517
|
| 871 |
+
2026-03-04 03:50:19.601888: val_loss 0.4561
|
| 872 |
+
2026-03-04 03:50:19.602368: Pseudo dice [np.float32(0.1685), np.float32(0.6949)]
|
| 873 |
+
2026-03-04 03:50:19.606785: Epoch time: 208.95 s
|
| 874 |
+
2026-03-04 03:50:23.023681:
|
| 875 |
+
2026-03-04 03:50:23.024375: Epoch 118
|
| 876 |
+
2026-03-04 03:50:23.024479: Current learning rate: 0.00448
|
| 877 |
+
2026-03-04 03:54:08.910030: train_loss 0.4678
|
| 878 |
+
2026-03-04 03:54:09.066763: val_loss 0.4626
|
| 879 |
+
2026-03-04 03:54:09.067959: Pseudo dice [np.float32(0.2478), np.float32(0.6643)]
|
| 880 |
+
2026-03-04 03:54:09.088500: Epoch time: 225.86 s
|
| 881 |
+
2026-03-04 03:54:09.089724: Yayy! New best EMA pseudo Dice: 0.4171999990940094
|
| 882 |
+
2026-03-04 03:54:18.565294:
|
| 883 |
+
2026-03-04 03:54:18.565435: Epoch 119
|
| 884 |
+
2026-03-04 03:54:18.565529: Current learning rate: 0.00443
|
| 885 |
+
2026-03-04 03:57:46.874587: train_loss 0.4571
|
| 886 |
+
2026-03-04 03:57:47.035897: val_loss 0.4476
|
| 887 |
+
2026-03-04 03:57:47.038113: Pseudo dice [np.float32(0.2191), np.float32(0.6381)]
|
| 888 |
+
2026-03-04 03:57:47.050705: Epoch time: 208.31 s
|
| 889 |
+
2026-03-04 03:57:47.050988: Yayy! New best EMA pseudo Dice: 0.41830000281333923
|
| 890 |
+
2026-03-04 03:57:54.379863:
|
| 891 |
+
2026-03-04 03:57:54.380172: Epoch 120
|
| 892 |
+
2026-03-04 03:57:54.380279: Current learning rate: 0.00438
|
| 893 |
+
2026-03-04 04:01:29.621863: train_loss 0.4739
|
| 894 |
+
2026-03-04 04:01:29.859064: val_loss 0.5037
|
| 895 |
+
2026-03-04 04:01:29.870987: Pseudo dice [np.float32(0.1578), np.float32(0.6427)]
|
| 896 |
+
2026-03-04 04:01:29.935881: Epoch time: 215.12 s
|
| 897 |
+
2026-03-04 04:01:33.758841:
|
| 898 |
+
2026-03-04 04:01:33.767093: Epoch 121
|
| 899 |
+
2026-03-04 04:01:33.767285: Current learning rate: 0.00433
|
| 900 |
+
2026-03-04 04:05:20.078977: train_loss 0.4753
|
| 901 |
+
2026-03-04 04:05:20.119541: val_loss 0.4699
|
| 902 |
+
2026-03-04 04:05:20.120425: Pseudo dice [np.float32(0.1821), np.float32(0.7007)]
|
| 903 |
+
2026-03-04 04:05:20.125219: Epoch time: 226.28 s
|
| 904 |
+
2026-03-04 04:05:20.126129: Yayy! New best EMA pseudo Dice: 0.4189999997615814
|
| 905 |
+
2026-03-04 04:05:29.749401:
|
| 906 |
+
2026-03-04 04:05:29.749568: Epoch 122
|
| 907 |
+
2026-03-04 04:05:29.749670: Current learning rate: 0.00429
|
| 908 |
+
2026-03-04 04:09:05.217775: train_loss 0.4411
|
| 909 |
+
2026-03-04 04:09:05.396823: val_loss 0.4882
|
| 910 |
+
2026-03-04 04:09:05.397084: Pseudo dice [np.float32(0.194), np.float32(0.6577)]
|
| 911 |
+
2026-03-04 04:09:05.420850: Epoch time: 215.47 s
|
| 912 |
+
2026-03-04 04:09:05.420985: Yayy! New best EMA pseudo Dice: 0.4196999967098236
|
| 913 |
+
2026-03-04 04:09:13.456343:
|
| 914 |
+
2026-03-04 04:09:13.456612: Epoch 123
|
| 915 |
+
2026-03-04 04:09:13.456730: Current learning rate: 0.00424
|
| 916 |
+
2026-03-04 04:12:54.657511: train_loss 0.4425
|
| 917 |
+
2026-03-04 04:12:54.877882: val_loss 0.4636
|
| 918 |
+
2026-03-04 04:12:54.881417: Pseudo dice [np.float32(0.2055), np.float32(0.6738)]
|
| 919 |
+
2026-03-04 04:12:54.932076: Epoch time: 221.11 s
|
| 920 |
+
2026-03-04 04:12:54.935853: Yayy! New best EMA pseudo Dice: 0.42170000076293945
|
| 921 |
+
2026-03-04 04:13:03.987174:
|
| 922 |
+
2026-03-04 04:13:03.987346: Epoch 124
|
| 923 |
+
2026-03-04 04:13:03.987446: Current learning rate: 0.00419
|
| 924 |
+
2026-03-04 04:16:29.629373: train_loss 0.479
|
| 925 |
+
2026-03-04 04:16:29.753414: val_loss 0.4937
|
| 926 |
+
2026-03-04 04:16:29.753715: Pseudo dice [np.float32(0.2122), np.float32(0.6402)]
|
| 927 |
+
2026-03-04 04:16:29.766015: Epoch time: 205.64 s
|
| 928 |
+
2026-03-04 04:16:29.770096: Yayy! New best EMA pseudo Dice: 0.4221000075340271
|
| 929 |
+
2026-03-04 04:16:37.050112:
|
| 930 |
+
2026-03-04 04:16:37.050811: Epoch 125
|
| 931 |
+
2026-03-04 04:16:37.050914: Current learning rate: 0.00414
|
| 932 |
+
2026-03-04 04:20:19.810744: train_loss 0.4659
|
| 933 |
+
2026-03-04 04:20:19.999572: val_loss 0.4434
|
| 934 |
+
2026-03-04 04:20:20.000730: Pseudo dice [np.float32(0.2306), np.float32(0.5797)]
|
| 935 |
+
2026-03-04 04:20:20.022767: Epoch time: 222.68 s
|
| 936 |
+
2026-03-04 04:20:23.852320:
|
| 937 |
+
2026-03-04 04:20:23.852482: Epoch 126
|
| 938 |
+
2026-03-04 04:20:23.852578: Current learning rate: 0.00409
|
| 939 |
+
2026-03-04 04:23:57.574826: train_loss 0.4707
|
| 940 |
+
2026-03-04 04:23:57.716540: val_loss 0.4839
|
| 941 |
+
2026-03-04 04:23:57.716802: Pseudo dice [np.float32(0.2456), np.float32(0.5592)]
|
| 942 |
+
2026-03-04 04:23:57.732128: Epoch time: 213.72 s
|
| 943 |
+
2026-03-04 04:24:01.440197:
|
| 944 |
+
2026-03-04 04:24:01.440419: Epoch 127
|
| 945 |
+
2026-03-04 04:24:01.440520: Current learning rate: 0.00404
|
| 946 |
+
2026-03-04 04:27:36.580485: train_loss 0.4652
|
| 947 |
+
2026-03-04 04:27:36.801755: val_loss 0.4823
|
| 948 |
+
2026-03-04 04:27:36.804586: Pseudo dice [np.float32(0.1774), np.float32(0.6421)]
|
| 949 |
+
2026-03-04 04:27:36.853514: Epoch time: 215.09 s
|
| 950 |
+
2026-03-04 04:28:08.977274:
|
| 951 |
+
2026-03-04 04:28:08.977477: Epoch 128
|
| 952 |
+
2026-03-04 04:28:08.977674: Current learning rate: 0.00399
|
| 953 |
+
2026-03-04 04:31:30.994004: train_loss 0.4689
|
| 954 |
+
2026-03-04 04:31:31.042505: val_loss 0.4549
|
| 955 |
+
2026-03-04 04:31:31.043032: Pseudo dice [np.float32(0.15), np.float32(0.689)]
|
| 956 |
+
2026-03-04 04:31:31.047136: Epoch time: 201.95 s
|
| 957 |
+
2026-03-04 04:31:35.484598:
|
| 958 |
+
2026-03-04 04:31:35.484754: Epoch 129
|
| 959 |
+
2026-03-04 04:31:35.484852: Current learning rate: 0.00394
|
| 960 |
+
2026-03-04 04:35:11.245578: train_loss 0.4581
|
| 961 |
+
2026-03-04 04:35:13.107995: val_loss 0.4728
|
| 962 |
+
2026-03-04 04:35:13.110256: Pseudo dice [np.float32(0.1789), np.float32(0.6832)]
|
| 963 |
+
2026-03-04 04:35:13.147811: Epoch time: 215.09 s
|
| 964 |
+
2026-03-04 04:35:17.294514:
|
| 965 |
+
2026-03-04 04:35:17.294731: Epoch 130
|
| 966 |
+
2026-03-04 04:35:17.294833: Current learning rate: 0.00389
|
| 967 |
+
2026-03-04 04:38:59.757598: train_loss 0.4471
|
| 968 |
+
2026-03-04 04:38:59.895087: val_loss 0.4306
|
| 969 |
+
2026-03-04 04:38:59.898372: Pseudo dice [np.float32(0.2135), np.float32(0.6056)]
|
| 970 |
+
2026-03-04 04:38:59.930571: Epoch time: 222.44 s
|
| 971 |
+
2026-03-04 04:39:04.225570:
|
| 972 |
+
2026-03-04 04:39:04.230494: Epoch 131
|
| 973 |
+
2026-03-04 04:39:04.230599: Current learning rate: 0.00384
|
| 974 |
+
2026-03-04 04:42:46.226659: train_loss 0.4577
|
| 975 |
+
2026-03-04 04:42:46.305733: val_loss 0.4431
|
| 976 |
+
2026-03-04 04:42:46.306303: Pseudo dice [np.float32(0.2051), np.float32(0.7067)]
|
| 977 |
+
2026-03-04 04:42:46.310443: Epoch time: 221.97 s
|
| 978 |
+
2026-03-04 04:42:50.975909:
|
| 979 |
+
2026-03-04 04:42:50.982741: Epoch 132
|
| 980 |
+
2026-03-04 04:42:50.982961: Current learning rate: 0.00379
|
| 981 |
+
2026-03-04 04:46:24.572701: train_loss 0.4578
|
| 982 |
+
2026-03-04 04:46:24.721227: val_loss 0.4741
|
| 983 |
+
2026-03-04 04:46:24.723291: Pseudo dice [np.float32(0.2057), np.float32(0.5769)]
|
| 984 |
+
2026-03-04 04:46:24.751218: Epoch time: 213.59 s
|
| 985 |
+
2026-03-04 04:46:29.043121:
|
| 986 |
+
2026-03-04 04:46:29.043964: Epoch 133
|
| 987 |
+
2026-03-04 04:46:29.044070: Current learning rate: 0.00374
|
| 988 |
+
2026-03-04 04:50:12.601359: train_loss 0.4401
|
| 989 |
+
2026-03-04 04:50:12.629722: val_loss 0.4249
|
| 990 |
+
2026-03-04 04:50:12.630234: Pseudo dice [np.float32(0.1851), np.float32(0.703)]
|
| 991 |
+
2026-03-04 04:50:12.634983: Epoch time: 223.56 s
|
| 992 |
+
2026-03-04 04:50:17.356036:
|
| 993 |
+
2026-03-04 04:50:17.361053: Epoch 134
|
| 994 |
+
2026-03-04 04:50:17.361189: Current learning rate: 0.00369
|
| 995 |
+
2026-03-04 04:53:51.888598: train_loss 0.4637
|
| 996 |
+
2026-03-04 04:53:52.048234: val_loss 0.4502
|
| 997 |
+
2026-03-04 04:53:52.051670: Pseudo dice [np.float32(0.243), np.float32(0.6822)]
|
| 998 |
+
2026-03-04 04:53:52.076410: Epoch time: 214.49 s
|
| 999 |
+
2026-03-04 04:53:52.078316: Yayy! New best EMA pseudo Dice: 0.42559999227523804
|
| 1000 |
+
2026-03-04 04:53:59.928379:
|
| 1001 |
+
2026-03-04 04:53:59.929279: Epoch 135
|
| 1002 |
+
2026-03-04 04:53:59.929383: Current learning rate: 0.00364
|
| 1003 |
+
2026-03-04 04:57:35.542993: train_loss 0.4323
|
| 1004 |
+
2026-03-04 04:57:35.715414: val_loss 0.4738
|
| 1005 |
+
2026-03-04 04:57:35.717248: Pseudo dice [np.float32(0.2253), np.float32(0.6442)]
|
| 1006 |
+
2026-03-04 04:57:35.742805: Epoch time: 215.59 s
|
| 1007 |
+
2026-03-04 04:57:35.745795: Yayy! New best EMA pseudo Dice: 0.42649999260902405
|
| 1008 |
+
2026-03-04 04:57:44.656275:
|
| 1009 |
+
2026-03-04 04:57:44.656466: Epoch 136
|
| 1010 |
+
2026-03-04 04:57:44.656597: Current learning rate: 0.00359
|
| 1011 |
+
2026-03-04 05:01:19.944190: train_loss 0.4429
|
| 1012 |
+
2026-03-04 05:01:20.079222: val_loss 0.416
|
| 1013 |
+
2026-03-04 05:01:20.086463: Pseudo dice [np.float32(0.26), np.float32(0.6489)]
|
| 1014 |
+
2026-03-04 05:01:20.100996: Epoch time: 215.29 s
|
| 1015 |
+
2026-03-04 05:01:20.101354: Yayy! New best EMA pseudo Dice: 0.4293000102043152
|
| 1016 |
+
2026-03-04 05:01:29.270242:
|
| 1017 |
+
2026-03-04 05:01:29.270531: Epoch 137
|
| 1018 |
+
2026-03-04 05:01:29.270633: Current learning rate: 0.00354
|
| 1019 |
+
2026-03-04 05:05:08.214831: train_loss 0.4356
|
| 1020 |
+
2026-03-04 05:05:08.487164: val_loss 0.4941
|
| 1021 |
+
2026-03-04 05:05:08.489108: Pseudo dice [np.float32(0.2437), np.float32(0.3686)]
|
| 1022 |
+
2026-03-04 05:05:08.526249: Epoch time: 218.87 s
|
| 1023 |
+
2026-03-04 05:05:12.603415:
|
| 1024 |
+
2026-03-04 05:05:12.604847: Epoch 138
|
| 1025 |
+
2026-03-04 05:05:12.604949: Current learning rate: 0.00349
|
| 1026 |
+
2026-03-04 05:08:49.211986: train_loss 0.4382
|
| 1027 |
+
2026-03-04 05:08:49.350902: val_loss 0.4523
|
| 1028 |
+
2026-03-04 05:08:49.354660: Pseudo dice [np.float32(0.2156), np.float32(0.6904)]
|
| 1029 |
+
2026-03-04 05:08:49.370976: Epoch time: 216.61 s
|
| 1030 |
+
2026-03-04 05:08:53.595706:
|
| 1031 |
+
2026-03-04 05:08:53.598762: Epoch 139
|
| 1032 |
+
2026-03-04 05:08:53.598901: Current learning rate: 0.00343
|
| 1033 |
+
2026-03-04 05:12:34.118242: train_loss 0.4679
|
| 1034 |
+
2026-03-04 05:12:34.225768: val_loss 0.4237
|
| 1035 |
+
2026-03-04 05:12:34.227662: Pseudo dice [np.float32(0.2254), np.float32(0.6138)]
|
| 1036 |
+
2026-03-04 05:12:34.259376: Epoch time: 220.51 s
|
| 1037 |
+
2026-03-04 05:12:38.574414:
|
| 1038 |
+
2026-03-04 05:12:38.576380: Epoch 140
|
| 1039 |
+
2026-03-04 05:12:38.576620: Current learning rate: 0.00338
|
| 1040 |
+
2026-03-04 05:16:06.683093: train_loss 0.4481
|
| 1041 |
+
2026-03-04 05:16:06.747788: val_loss 0.4515
|
| 1042 |
+
2026-03-04 05:16:06.747953: Pseudo dice [np.float32(0.2381), np.float32(0.68)]
|
| 1043 |
+
2026-03-04 05:16:06.757217: Epoch time: 208.11 s
|
| 1044 |
+
2026-03-04 05:16:10.252192:
|
| 1045 |
+
2026-03-04 05:16:10.252374: Epoch 141
|
| 1046 |
+
2026-03-04 05:16:10.252477: Current learning rate: 0.00333
|
| 1047 |
+
2026-03-04 05:19:53.897729: train_loss 0.4645
|
| 1048 |
+
2026-03-04 05:19:54.093175: val_loss 0.4354
|
| 1049 |
+
2026-03-04 05:19:54.094593: Pseudo dice [np.float32(0.2403), np.float32(0.6635)]
|
| 1050 |
+
2026-03-04 05:19:54.124612: Epoch time: 223.61 s
|
| 1051 |
+
2026-03-04 05:20:26.192698:
|
| 1052 |
+
2026-03-04 05:20:26.193008: Epoch 142
|
| 1053 |
+
2026-03-04 05:20:26.193136: Current learning rate: 0.00328
|
| 1054 |
+
2026-03-04 05:23:42.608431: train_loss 0.4469
|
| 1055 |
+
2026-03-04 05:23:42.699343: val_loss 0.5209
|
| 1056 |
+
2026-03-04 05:23:42.700348: Pseudo dice [np.float32(0.1649), np.float32(0.6458)]
|
| 1057 |
+
2026-03-04 05:23:42.707007: Epoch time: 196.41 s
|
| 1058 |
+
2026-03-04 05:23:47.764033:
|
| 1059 |
+
2026-03-04 05:23:47.764302: Epoch 143
|
| 1060 |
+
2026-03-04 05:23:47.764402: Current learning rate: 0.00323
|
| 1061 |
+
2026-03-04 05:27:33.362347: train_loss 0.4394
|
| 1062 |
+
2026-03-04 05:27:33.677618: val_loss 0.4433
|
| 1063 |
+
2026-03-04 05:27:33.683546: Pseudo dice [np.float32(0.1977), np.float32(0.7073)]
|
| 1064 |
+
2026-03-04 05:27:33.709849: Epoch time: 225.51 s
|
| 1065 |
+
2026-03-04 05:27:38.053666:
|
| 1066 |
+
2026-03-04 05:27:38.054405: Epoch 144
|
| 1067 |
+
2026-03-04 05:27:38.054504: Current learning rate: 0.00318
|
| 1068 |
+
2026-03-04 05:31:06.351961: train_loss 0.4781
|
| 1069 |
+
2026-03-04 05:31:06.521901: val_loss 0.5004
|
| 1070 |
+
2026-03-04 05:31:06.526532: Pseudo dice [np.float32(0.1677), np.float32(0.4578)]
|
| 1071 |
+
2026-03-04 05:31:06.540496: Epoch time: 208.3 s
|
| 1072 |
+
2026-03-04 05:31:09.836497:
|
| 1073 |
+
2026-03-04 05:31:09.837493: Epoch 145
|
| 1074 |
+
2026-03-04 05:31:09.837598: Current learning rate: 0.00313
|
| 1075 |
+
2026-03-04 05:34:55.187284: train_loss 0.449
|
| 1076 |
+
2026-03-04 05:34:55.274971: val_loss 0.4689
|
| 1077 |
+
2026-03-04 05:34:55.275687: Pseudo dice [np.float32(0.2336), np.float32(0.6781)]
|
| 1078 |
+
2026-03-04 05:34:55.279612: Epoch time: 225.3 s
|
| 1079 |
+
2026-03-04 05:35:00.391057:
|
| 1080 |
+
2026-03-04 05:35:00.403568: Epoch 146
|
| 1081 |
+
2026-03-04 05:35:00.404293: Current learning rate: 0.00308
|
| 1082 |
+
2026-03-04 05:38:34.705757: train_loss 0.4252
|
| 1083 |
+
2026-03-04 05:38:34.914760: val_loss 0.4282
|
| 1084 |
+
2026-03-04 05:38:34.918475: Pseudo dice [np.float32(0.2215), np.float32(0.6897)]
|
| 1085 |
+
2026-03-04 05:38:34.945584: Epoch time: 214.32 s
|
| 1086 |
+
2026-03-04 05:38:38.967250:
|
| 1087 |
+
2026-03-04 05:38:38.968501: Epoch 147
|
| 1088 |
+
2026-03-04 05:38:38.968605: Current learning rate: 0.00303
|
| 1089 |
+
2026-03-04 05:42:19.692967: train_loss 0.4536
|
| 1090 |
+
2026-03-04 05:42:19.792005: val_loss 0.4317
|
| 1091 |
+
2026-03-04 05:42:19.793231: Pseudo dice [np.float32(0.2678), np.float32(0.6743)]
|
| 1092 |
+
2026-03-04 05:42:19.797919: Epoch time: 220.68 s
|
| 1093 |
+
2026-03-04 05:42:24.957923:
|
| 1094 |
+
2026-03-04 05:42:24.969552: Epoch 148
|
| 1095 |
+
2026-03-04 05:42:24.969742: Current learning rate: 0.00297
|
| 1096 |
+
2026-03-04 05:45:52.910822: train_loss 0.4431
|
| 1097 |
+
2026-03-04 05:45:53.071593: val_loss 0.4665
|
| 1098 |
+
2026-03-04 05:45:53.071910: Pseudo dice [np.float32(0.2666), np.float32(0.6488)]
|
| 1099 |
+
2026-03-04 05:45:53.087416: Epoch time: 207.96 s
|
| 1100 |
+
2026-03-04 05:45:53.087613: Yayy! New best EMA pseudo Dice: 0.43140000104904175
|
| 1101 |
+
2026-03-04 05:46:00.431642:
|
| 1102 |
+
2026-03-04 05:46:00.431902: Epoch 149
|
| 1103 |
+
2026-03-04 05:46:00.432390: Current learning rate: 0.00292
|
| 1104 |
+
2026-03-04 05:49:44.727044: train_loss 0.4349
|
| 1105 |
+
2026-03-04 05:49:44.974344: val_loss 0.4475
|
| 1106 |
+
2026-03-04 05:49:44.990253: Pseudo dice [np.float32(0.299), np.float32(0.662)]
|
| 1107 |
+
2026-03-04 05:49:45.026030: Epoch time: 224.24 s
|
| 1108 |
+
2026-03-04 05:49:50.243942: Yayy! New best EMA pseudo Dice: 0.43630000948905945
|
| 1109 |
+
2026-03-04 05:49:55.767834:
|
| 1110 |
+
2026-03-04 05:49:55.767954: Epoch 150
|
| 1111 |
+
2026-03-04 05:49:55.768050: Current learning rate: 0.00287
|
| 1112 |
+
2026-03-04 05:53:31.356162: train_loss 0.4644
|
| 1113 |
+
2026-03-04 05:53:31.480582: val_loss 0.4954
|
| 1114 |
+
2026-03-04 05:53:31.483115: Pseudo dice [np.float32(0.2482), np.float32(0.6283)]
|
| 1115 |
+
2026-03-04 05:53:31.497730: Epoch time: 215.59 s
|
| 1116 |
+
2026-03-04 05:53:31.497910: Yayy! New best EMA pseudo Dice: 0.43650001287460327
|
| 1117 |
+
2026-03-04 05:53:39.578274:
|
| 1118 |
+
2026-03-04 05:53:39.578509: Epoch 151
|
| 1119 |
+
2026-03-04 05:53:39.578606: Current learning rate: 0.00282
|
| 1120 |
+
2026-03-04 05:57:16.655391: train_loss 0.4284
|
| 1121 |
+
2026-03-04 05:57:16.812109: val_loss 0.4248
|
| 1122 |
+
2026-03-04 05:57:16.812355: Pseudo dice [np.float32(0.2509), np.float32(0.6645)]
|
| 1123 |
+
2026-03-04 05:57:16.831262: Epoch time: 217.06 s
|
| 1124 |
+
2026-03-04 05:57:16.831492: Yayy! New best EMA pseudo Dice: 0.43860000371932983
|
| 1125 |
+
2026-03-04 05:57:25.178845:
|
| 1126 |
+
2026-03-04 05:57:25.179055: Epoch 152
|
| 1127 |
+
2026-03-04 05:57:25.179182: Current learning rate: 0.00277
|
| 1128 |
+
2026-03-04 06:01:00.325711: train_loss 0.4492
|
| 1129 |
+
2026-03-04 06:01:00.635844: val_loss 0.4611
|
| 1130 |
+
2026-03-04 06:01:00.635984: Pseudo dice [np.float32(0.2825), np.float32(0.5971)]
|
| 1131 |
+
2026-03-04 06:01:00.665112: Epoch time: 215.13 s
|
| 1132 |
+
2026-03-04 06:01:00.665282: Yayy! New best EMA pseudo Dice: 0.43869999051094055
|
| 1133 |
+
2026-03-04 06:01:08.647546:
|
| 1134 |
+
2026-03-04 06:01:08.647798: Epoch 153
|
| 1135 |
+
2026-03-04 06:01:08.647899: Current learning rate: 0.00272
|
| 1136 |
+
2026-03-04 06:04:45.493291: train_loss 0.4396
|
| 1137 |
+
2026-03-04 06:04:45.711833: val_loss 0.4564
|
| 1138 |
+
2026-03-04 06:04:45.717979: Pseudo dice [np.float32(0.2342), np.float32(0.6235)]
|
| 1139 |
+
2026-03-04 06:04:45.743617: Epoch time: 216.84 s
|
| 1140 |
+
2026-03-04 06:04:49.549164:
|
| 1141 |
+
2026-03-04 06:04:49.549272: Epoch 154
|
| 1142 |
+
2026-03-04 06:04:49.549368: Current learning rate: 0.00266
|
| 1143 |
+
2026-03-04 06:08:30.878613: train_loss 0.4476
|
| 1144 |
+
2026-03-04 06:08:31.099738: val_loss 0.4369
|
| 1145 |
+
2026-03-04 06:08:31.102591: Pseudo dice [np.float32(0.2378), np.float32(0.6842)]
|
| 1146 |
+
2026-03-04 06:08:31.175293: Epoch time: 221.25 s
|
| 1147 |
+
2026-03-04 06:08:31.175822: Yayy! New best EMA pseudo Dice: 0.4401000142097473
|
| 1148 |
+
2026-03-04 06:08:40.576396:
|
| 1149 |
+
2026-03-04 06:08:40.576574: Epoch 155
|
| 1150 |
+
2026-03-04 06:08:40.576729: Current learning rate: 0.00261
|
| 1151 |
+
2026-03-04 06:12:13.330639: train_loss 0.4492
|
| 1152 |
+
2026-03-04 06:12:13.502081: val_loss 0.4405
|
| 1153 |
+
2026-03-04 06:12:13.502511: Pseudo dice [np.float32(0.2723), np.float32(0.6)]
|
| 1154 |
+
2026-03-04 06:12:13.570524: Epoch time: 212.7 s
|
| 1155 |
+
2026-03-04 06:12:17.166063:
|
| 1156 |
+
2026-03-04 06:12:17.166291: Epoch 156
|
| 1157 |
+
2026-03-04 06:12:17.166404: Current learning rate: 0.00256
|
| 1158 |
+
2026-03-04 06:15:49.597511: train_loss 0.4294
|
| 1159 |
+
2026-03-04 06:15:49.759014: val_loss 0.47
|
| 1160 |
+
2026-03-04 06:15:49.759237: Pseudo dice [np.float32(0.2613), np.float32(0.6089)]
|
| 1161 |
+
2026-03-04 06:15:49.778394: Epoch time: 212.43 s
|
| 1162 |
+
2026-03-04 06:16:22.728495:
|
| 1163 |
+
2026-03-04 06:16:22.728638: Epoch 157
|
| 1164 |
+
2026-03-04 06:16:22.728777: Current learning rate: 0.00251
|
| 1165 |
+
2026-03-04 06:19:49.306860: train_loss 0.429
|
| 1166 |
+
2026-03-04 06:19:49.382909: val_loss 0.4486
|
| 1167 |
+
2026-03-04 06:19:49.383230: Pseudo dice [np.float32(0.2599), np.float32(0.5999)]
|
| 1168 |
+
2026-03-04 06:19:49.394600: Epoch time: 206.56 s
|
| 1169 |
+
2026-03-04 06:19:53.762813:
|
| 1170 |
+
2026-03-04 06:19:53.763099: Epoch 158
|
| 1171 |
+
2026-03-04 06:19:53.763237: Current learning rate: 0.00245
|
| 1172 |
+
2026-03-04 06:23:43.304166: train_loss 0.4218
|
| 1173 |
+
2026-03-04 06:23:43.436394: val_loss 0.4499
|
| 1174 |
+
2026-03-04 06:23:43.436632: Pseudo dice [np.float32(0.2268), np.float32(0.6401)]
|
| 1175 |
+
2026-03-04 06:23:43.456966: Epoch time: 229.54 s
|
| 1176 |
+
2026-03-04 06:23:48.057629:
|
| 1177 |
+
2026-03-04 06:23:48.059601: Epoch 159
|
| 1178 |
+
2026-03-04 06:23:48.059704: Current learning rate: 0.0024
|
| 1179 |
+
2026-03-04 06:27:17.865030: train_loss 0.4438
|
| 1180 |
+
2026-03-04 06:27:18.051600: val_loss 0.4343
|
| 1181 |
+
2026-03-04 06:27:18.051752: Pseudo dice [np.float32(0.2493), np.float32(0.6522)]
|
| 1182 |
+
2026-03-04 06:27:18.070197: Epoch time: 209.81 s
|
| 1183 |
+
2026-03-04 06:27:22.849953:
|
| 1184 |
+
2026-03-04 06:27:22.857926: Epoch 160
|
| 1185 |
+
2026-03-04 06:27:22.858081: Current learning rate: 0.00235
|
| 1186 |
+
2026-03-04 06:31:05.783946: train_loss 0.4321
|
| 1187 |
+
2026-03-04 06:31:05.927817: val_loss 0.4524
|
| 1188 |
+
2026-03-04 06:31:05.928263: Pseudo dice [np.float32(0.2545), np.float32(0.6461)]
|
| 1189 |
+
2026-03-04 06:31:05.934826: Epoch time: 222.8 s
|
| 1190 |
+
2026-03-04 06:31:05.936185: Yayy! New best EMA pseudo Dice: 0.44020000100135803
|
| 1191 |
+
2026-03-04 06:31:15.924038:
|
| 1192 |
+
2026-03-04 06:31:15.924201: Epoch 161
|
| 1193 |
+
2026-03-04 06:31:15.924298: Current learning rate: 0.0023
|
| 1194 |
+
2026-03-04 06:34:52.778385: train_loss 0.4333
|
| 1195 |
+
2026-03-04 06:34:52.896855: val_loss 0.3966
|
| 1196 |
+
2026-03-04 06:34:52.897210: Pseudo dice [np.float32(0.2681), np.float32(0.6868)]
|
| 1197 |
+
2026-03-04 06:34:52.907275: Epoch time: 216.85 s
|
| 1198 |
+
2026-03-04 06:34:52.907479: Yayy! New best EMA pseudo Dice: 0.4438999891281128
|
| 1199 |
+
2026-03-04 06:35:00.998303:
|
| 1200 |
+
2026-03-04 06:35:00.998622: Epoch 162
|
| 1201 |
+
2026-03-04 06:35:00.998724: Current learning rate: 0.00224
|
| 1202 |
+
2026-03-04 06:38:53.403717: train_loss 0.4302
|
| 1203 |
+
2026-03-04 06:38:53.524001: val_loss 0.4319
|
| 1204 |
+
2026-03-04 06:38:53.524237: Pseudo dice [np.float32(0.2552), np.float32(0.6652)]
|
| 1205 |
+
2026-03-04 06:38:53.555912: Epoch time: 232.4 s
|
| 1206 |
+
2026-03-04 06:38:53.562464: Yayy! New best EMA pseudo Dice: 0.4456000030040741
|
| 1207 |
+
2026-03-04 06:39:02.759073:
|
| 1208 |
+
2026-03-04 06:39:02.759272: Epoch 163
|
| 1209 |
+
2026-03-04 06:39:02.759374: Current learning rate: 0.00219
|
| 1210 |
+
2026-03-04 06:42:34.657657: train_loss 0.4444
|
| 1211 |
+
2026-03-04 06:42:34.828354: val_loss 0.418
|
| 1212 |
+
2026-03-04 06:42:34.828634: Pseudo dice [np.float32(0.237), np.float32(0.6974)]
|
| 1213 |
+
2026-03-04 06:42:34.841029: Epoch time: 211.9 s
|
| 1214 |
+
2026-03-04 06:42:34.841310: Yayy! New best EMA pseudo Dice: 0.44769999384880066
|
| 1215 |
+
2026-03-04 06:42:43.316745:
|
| 1216 |
+
2026-03-04 06:42:43.317009: Epoch 164
|
| 1217 |
+
2026-03-04 06:42:43.317110: Current learning rate: 0.00214
|
| 1218 |
+
2026-03-04 06:46:23.706785: train_loss 0.4243
|
| 1219 |
+
2026-03-04 06:46:23.938641: val_loss 0.4338
|
| 1220 |
+
2026-03-04 06:46:23.941432: Pseudo dice [np.float32(0.2898), np.float32(0.6395)]
|
| 1221 |
+
2026-03-04 06:46:23.990647: Epoch time: 220.34 s
|
| 1222 |
+
2026-03-04 06:46:23.992966: Yayy! New best EMA pseudo Dice: 0.44940000772476196
|
| 1223 |
+
2026-03-04 06:46:32.703000:
|
| 1224 |
+
2026-03-04 06:46:32.703294: Epoch 165
|
| 1225 |
+
2026-03-04 06:46:32.703396: Current learning rate: 0.00208
|
| 1226 |
+
2026-03-04 06:49:58.801764: train_loss 0.4379
|
| 1227 |
+
2026-03-04 06:49:58.988679: val_loss 0.4286
|
| 1228 |
+
2026-03-04 06:49:58.988955: Pseudo dice [np.float32(0.1858), np.float32(0.7313)]
|
| 1229 |
+
2026-03-04 06:49:59.002477: Epoch time: 206.1 s
|
| 1230 |
+
2026-03-04 06:49:59.002870: Yayy! New best EMA pseudo Dice: 0.450300008058548
|
| 1231 |
+
2026-03-04 06:50:05.592320:
|
| 1232 |
+
2026-03-04 06:50:05.592557: Epoch 166
|
| 1233 |
+
2026-03-04 06:50:05.593074: Current learning rate: 0.00203
|
| 1234 |
+
2026-03-04 06:53:46.586087: train_loss 0.4491
|
| 1235 |
+
2026-03-04 06:53:46.633119: val_loss 0.4288
|
| 1236 |
+
2026-03-04 06:53:46.634310: Pseudo dice [np.float32(0.2732), np.float32(0.6181)]
|
| 1237 |
+
2026-03-04 06:53:46.638361: Epoch time: 220.98 s
|
| 1238 |
+
2026-03-04 06:53:51.184122:
|
| 1239 |
+
2026-03-04 06:53:51.184264: Epoch 167
|
| 1240 |
+
2026-03-04 06:53:51.184359: Current learning rate: 0.00198
|
| 1241 |
+
2026-03-04 06:57:19.588857: train_loss 0.4258
|
| 1242 |
+
2026-03-04 06:57:19.692944: val_loss 0.4122
|
| 1243 |
+
2026-03-04 06:57:19.693083: Pseudo dice [np.float32(0.2273), np.float32(0.6861)]
|
| 1244 |
+
2026-03-04 06:57:19.714391: Epoch time: 208.41 s
|
| 1245 |
+
2026-03-04 06:57:19.716092: Yayy! New best EMA pseudo Dice: 0.4505000114440918
|
| 1246 |
+
2026-03-04 06:57:27.631428:
|
| 1247 |
+
2026-03-04 06:57:27.631608: Epoch 168
|
| 1248 |
+
2026-03-04 06:57:27.631708: Current learning rate: 0.00192
|
| 1249 |
+
2026-03-04 07:01:08.240182: train_loss 0.4151
|
| 1250 |
+
2026-03-04 07:01:08.376826: val_loss 0.4698
|
| 1251 |
+
2026-03-04 07:01:08.377184: Pseudo dice [np.float32(0.2376), np.float32(0.6645)]
|
| 1252 |
+
2026-03-04 07:01:08.404660: Epoch time: 220.52 s
|
| 1253 |
+
2026-03-04 07:01:08.405485: Yayy! New best EMA pseudo Dice: 0.4505999982357025
|
| 1254 |
+
2026-03-04 07:01:17.271780:
|
| 1255 |
+
2026-03-04 07:01:17.271929: Epoch 169
|
| 1256 |
+
2026-03-04 07:01:17.272026: Current learning rate: 0.00187
|
| 1257 |
+
2026-03-04 07:04:51.133435: train_loss 0.4488
|
| 1258 |
+
2026-03-04 07:04:51.231666: val_loss 0.422
|
| 1259 |
+
2026-03-04 07:04:51.231769: Pseudo dice [np.float32(0.2567), np.float32(0.6682)]
|
| 1260 |
+
2026-03-04 07:04:51.241536: Epoch time: 213.87 s
|
| 1261 |
+
2026-03-04 07:04:51.241754: Yayy! New best EMA pseudo Dice: 0.45179998874664307
|
| 1262 |
+
2026-03-04 07:04:58.889243:
|
| 1263 |
+
2026-03-04 07:04:58.889445: Epoch 170
|
| 1264 |
+
2026-03-04 07:04:58.889543: Current learning rate: 0.00181
|
| 1265 |
+
2026-03-04 07:08:47.110201: train_loss 0.4301
|
| 1266 |
+
2026-03-04 07:08:47.266447: val_loss 0.4342
|
| 1267 |
+
2026-03-04 07:08:47.352212: Pseudo dice [np.float32(0.2778), np.float32(0.6594)]
|
| 1268 |
+
2026-03-04 07:08:47.358951: Epoch time: 228.14 s
|
| 1269 |
+
2026-03-04 07:08:47.359183: Yayy! New best EMA pseudo Dice: 0.45350000262260437
|
| 1270 |
+
2026-03-04 07:09:24.674665:
|
| 1271 |
+
2026-03-04 07:09:24.674903: Epoch 171
|
| 1272 |
+
2026-03-04 07:09:24.675006: Current learning rate: 0.00176
|
| 1273 |
+
2026-03-04 07:12:51.605803: train_loss 0.4213
|
| 1274 |
+
2026-03-04 07:12:51.808672: val_loss 0.4376
|
| 1275 |
+
2026-03-04 07:12:51.808911: Pseudo dice [np.float32(0.274), np.float32(0.6514)]
|
| 1276 |
+
2026-03-04 07:12:51.824486: Epoch time: 206.88 s
|
| 1277 |
+
2026-03-04 07:12:51.825465: Yayy! New best EMA pseudo Dice: 0.4544000029563904
|
| 1278 |
+
2026-03-04 07:13:00.305816:
|
| 1279 |
+
2026-03-04 07:13:00.306054: Epoch 172
|
| 1280 |
+
2026-03-04 07:13:00.306174: Current learning rate: 0.0017
|
| 1281 |
+
2026-03-04 07:16:31.936288: train_loss 0.452
|
| 1282 |
+
2026-03-04 07:16:32.109759: val_loss 0.4257
|
| 1283 |
+
2026-03-04 07:16:32.110205: Pseudo dice [np.float32(0.2785), np.float32(0.5587)]
|
| 1284 |
+
2026-03-04 07:16:32.113893: Epoch time: 211.55 s
|
| 1285 |
+
2026-03-04 07:16:36.939283:
|
| 1286 |
+
2026-03-04 07:16:36.947320: Epoch 173
|
| 1287 |
+
2026-03-04 07:16:36.947428: Current learning rate: 0.00165
|
| 1288 |
+
2026-03-04 07:20:19.219618: train_loss 0.4383
|
| 1289 |
+
2026-03-04 07:20:19.336707: val_loss 0.3933
|
| 1290 |
+
2026-03-04 07:20:19.338204: Pseudo dice [np.float32(0.2923), np.float32(0.6768)]
|
| 1291 |
+
2026-03-04 07:20:19.354768: Epoch time: 222.28 s
|
| 1292 |
+
2026-03-04 07:20:23.265496:
|
| 1293 |
+
2026-03-04 07:20:23.268633: Epoch 174
|
| 1294 |
+
2026-03-04 07:20:23.268800: Current learning rate: 0.00159
|
| 1295 |
+
2026-03-04 07:24:07.565426: train_loss 0.4262
|
| 1296 |
+
2026-03-04 07:24:07.695704: val_loss 0.3757
|
| 1297 |
+
2026-03-04 07:24:07.704452: Pseudo dice [np.float32(0.2995), np.float32(0.6714)]
|
| 1298 |
+
2026-03-04 07:24:07.733646: Epoch time: 224.25 s
|
| 1299 |
+
2026-03-04 07:24:07.733847: Yayy! New best EMA pseudo Dice: 0.45730000734329224
|
| 1300 |
+
2026-03-04 07:24:16.581037:
|
| 1301 |
+
2026-03-04 07:24:16.581319: Epoch 175
|
| 1302 |
+
2026-03-04 07:24:16.581420: Current learning rate: 0.00154
|
| 1303 |
+
2026-03-04 07:27:59.248112: train_loss 0.4225
|
| 1304 |
+
2026-03-04 07:27:59.461097: val_loss 0.4346
|
| 1305 |
+
2026-03-04 07:27:59.464321: Pseudo dice [np.float32(0.2143), np.float32(0.6808)]
|
| 1306 |
+
2026-03-04 07:27:59.497037: Epoch time: 222.63 s
|
| 1307 |
+
2026-03-04 07:28:04.346507:
|
| 1308 |
+
2026-03-04 07:28:04.351461: Epoch 176
|
| 1309 |
+
2026-03-04 07:28:04.351573: Current learning rate: 0.00148
|
| 1310 |
+
2026-03-04 07:31:49.343758: train_loss 0.4368
|
| 1311 |
+
2026-03-04 07:31:49.561137: val_loss 0.417
|
| 1312 |
+
2026-03-04 07:31:49.562284: Pseudo dice [np.float32(0.2816), np.float32(0.5755)]
|
| 1313 |
+
2026-03-04 07:31:49.582449: Epoch time: 224.93 s
|
| 1314 |
+
2026-03-04 07:31:53.783762:
|
| 1315 |
+
2026-03-04 07:31:53.802250: Epoch 177
|
| 1316 |
+
2026-03-04 07:31:53.802369: Current learning rate: 0.00143
|
| 1317 |
+
2026-03-04 07:35:28.697205: train_loss 0.4378
|
| 1318 |
+
2026-03-04 07:35:28.861783: val_loss 0.4085
|
| 1319 |
+
2026-03-04 07:35:28.862001: Pseudo dice [np.float32(0.3009), np.float32(0.6577)]
|
| 1320 |
+
2026-03-04 07:35:28.866535: Epoch time: 214.92 s
|
| 1321 |
+
2026-03-04 07:35:32.400720:
|
| 1322 |
+
2026-03-04 07:35:32.401377: Epoch 178
|
| 1323 |
+
2026-03-04 07:35:32.401483: Current learning rate: 0.00137
|
| 1324 |
+
2026-03-04 07:39:23.511752: train_loss 0.4218
|
| 1325 |
+
2026-03-04 07:39:23.666690: val_loss 0.4352
|
| 1326 |
+
2026-03-04 07:39:23.667363: Pseudo dice [np.float32(0.2948), np.float32(0.6394)]
|
| 1327 |
+
2026-03-04 07:39:23.672270: Epoch time: 231.08 s
|
| 1328 |
+
2026-03-04 07:39:28.697474:
|
| 1329 |
+
2026-03-04 07:39:28.707586: Epoch 179
|
| 1330 |
+
2026-03-04 07:39:28.707699: Current learning rate: 0.00132
|
| 1331 |
+
2026-03-04 07:42:56.626967: train_loss 0.4101
|
| 1332 |
+
2026-03-04 07:42:56.769442: val_loss 0.4118
|
| 1333 |
+
2026-03-04 07:42:56.770904: Pseudo dice [np.float32(0.2923), np.float32(0.5938)]
|
| 1334 |
+
2026-03-04 07:42:56.784883: Epoch time: 207.93 s
|
| 1335 |
+
2026-03-04 07:43:01.160418:
|
| 1336 |
+
2026-03-04 07:43:01.160636: Epoch 180
|
| 1337 |
+
2026-03-04 07:43:01.160758: Current learning rate: 0.00126
|
| 1338 |
+
2026-03-04 07:46:41.896617: train_loss 0.417
|
| 1339 |
+
2026-03-04 07:46:42.068987: val_loss 0.3933
|
| 1340 |
+
2026-03-04 07:46:42.075632: Pseudo dice [np.float32(0.2893), np.float32(0.6766)]
|
| 1341 |
+
2026-03-04 07:46:42.090836: Epoch time: 220.65 s
|
| 1342 |
+
2026-03-04 07:46:42.091171: Yayy! New best EMA pseudo Dice: 0.4584999978542328
|
| 1343 |
+
2026-03-04 07:46:51.016639:
|
| 1344 |
+
2026-03-04 07:46:51.018599: Epoch 181
|
| 1345 |
+
2026-03-04 07:46:51.018705: Current learning rate: 0.0012
|
| 1346 |
+
2026-03-04 07:50:18.710249: train_loss 0.4065
|
| 1347 |
+
2026-03-04 07:50:18.868811: val_loss 0.4851
|
| 1348 |
+
2026-03-04 07:50:18.868975: Pseudo dice [np.float32(0.2572), np.float32(0.6154)]
|
| 1349 |
+
2026-03-04 07:50:18.887282: Epoch time: 207.69 s
|
| 1350 |
+
2026-03-04 07:50:22.746291:
|
| 1351 |
+
2026-03-04 07:50:22.746537: Epoch 182
|
| 1352 |
+
2026-03-04 07:50:22.746638: Current learning rate: 0.00115
|
| 1353 |
+
2026-03-04 07:54:08.651574: train_loss 0.432
|
| 1354 |
+
2026-03-04 07:54:08.862666: val_loss 0.386
|
| 1355 |
+
2026-03-04 07:54:08.863825: Pseudo dice [np.float32(0.29), np.float32(0.67)]
|
| 1356 |
+
2026-03-04 07:54:08.885279: Epoch time: 225.86 s
|
| 1357 |
+
2026-03-04 07:54:08.889933: Yayy! New best EMA pseudo Dice: 0.4587000012397766
|
| 1358 |
+
2026-03-04 07:54:17.328321:
|
| 1359 |
+
2026-03-04 07:54:17.328507: Epoch 183
|
| 1360 |
+
2026-03-04 07:54:17.328609: Current learning rate: 0.00109
|
| 1361 |
+
2026-03-04 07:57:38.969470: train_loss 0.4394
|
| 1362 |
+
2026-03-04 07:57:39.048306: val_loss 0.4704
|
| 1363 |
+
2026-03-04 07:57:39.051328: Pseudo dice [np.float32(0.2656), np.float32(0.6355)]
|
| 1364 |
+
2026-03-04 07:57:39.059327: Epoch time: 201.64 s
|
| 1365 |
+
2026-03-04 07:57:42.876221:
|
| 1366 |
+
2026-03-04 07:57:42.876468: Epoch 184
|
| 1367 |
+
2026-03-04 07:57:42.876573: Current learning rate: 0.00103
|
| 1368 |
+
2026-03-04 08:01:28.063421: train_loss 0.4234
|
| 1369 |
+
2026-03-04 08:01:28.260056: val_loss 0.4679
|
| 1370 |
+
2026-03-04 08:01:28.263004: Pseudo dice [np.float32(0.2502), np.float32(0.6232)]
|
| 1371 |
+
2026-03-04 08:01:28.341224: Epoch time: 225.09 s
|
| 1372 |
+
2026-03-04 08:02:00.788910:
|
| 1373 |
+
2026-03-04 08:02:00.789121: Epoch 185
|
| 1374 |
+
2026-03-04 08:02:00.789258: Current learning rate: 0.00097
|
| 1375 |
+
2026-03-04 08:05:04.530500: train_loss 0.4095
|
| 1376 |
+
2026-03-04 08:05:04.670882: val_loss 0.4137
|
| 1377 |
+
2026-03-04 08:05:04.671134: Pseudo dice [np.float32(0.2922), np.float32(0.6563)]
|
| 1378 |
+
2026-03-04 08:05:04.686255: Epoch time: 183.74 s
|
| 1379 |
+
2026-03-04 08:05:07.974540:
|
| 1380 |
+
2026-03-04 08:05:07.974750: Epoch 186
|
| 1381 |
+
2026-03-04 08:05:07.974845: Current learning rate: 0.00091
|
| 1382 |
+
2026-03-04 08:08:57.396213: train_loss 0.4058
|
| 1383 |
+
2026-03-04 08:08:57.499727: val_loss 0.4228
|
| 1384 |
+
2026-03-04 08:08:57.500413: Pseudo dice [np.float32(0.274), np.float32(0.6752)]
|
| 1385 |
+
2026-03-04 08:08:57.506503: Epoch time: 229.37 s
|
| 1386 |
+
2026-03-04 08:08:57.507601: Yayy! New best EMA pseudo Dice: 0.4593000113964081
|
| 1387 |
+
2026-03-04 08:09:06.661554:
|
| 1388 |
+
2026-03-04 08:09:06.662286: Epoch 187
|
| 1389 |
+
2026-03-04 08:09:06.662385: Current learning rate: 0.00085
|
| 1390 |
+
2026-03-04 08:12:42.939772: train_loss 0.4223
|
| 1391 |
+
2026-03-04 08:12:43.170264: val_loss 0.3518
|
| 1392 |
+
2026-03-04 08:12:43.175574: Pseudo dice [np.float32(0.2809), np.float32(0.7069)]
|
| 1393 |
+
2026-03-04 08:12:43.221982: Epoch time: 216.2 s
|
| 1394 |
+
2026-03-04 08:12:43.222170: Yayy! New best EMA pseudo Dice: 0.462799996137619
|
| 1395 |
+
2026-03-04 08:12:51.077548:
|
| 1396 |
+
2026-03-04 08:12:51.077729: Epoch 188
|
| 1397 |
+
2026-03-04 08:12:51.077828: Current learning rate: 0.00079
|
| 1398 |
+
2026-03-04 08:16:28.224962: train_loss 0.3987
|
| 1399 |
+
2026-03-04 08:16:28.318377: val_loss 0.411
|
| 1400 |
+
2026-03-04 08:16:28.324389: Pseudo dice [np.float32(0.3106), np.float32(0.6455)]
|
| 1401 |
+
2026-03-04 08:16:28.350066: Epoch time: 217.14 s
|
| 1402 |
+
2026-03-04 08:16:28.350396: Yayy! New best EMA pseudo Dice: 0.4643000066280365
|
| 1403 |
+
2026-03-04 08:16:36.204123:
|
| 1404 |
+
2026-03-04 08:16:36.204390: Epoch 189
|
| 1405 |
+
2026-03-04 08:16:36.204496: Current learning rate: 0.00074
|
| 1406 |
+
2026-03-04 08:20:20.539586: train_loss 0.4244
|
| 1407 |
+
2026-03-04 08:20:20.697363: val_loss 0.402
|
| 1408 |
+
2026-03-04 08:20:20.697525: Pseudo dice [np.float32(0.3034), np.float32(0.6515)]
|
| 1409 |
+
2026-03-04 08:20:20.716508: Epoch time: 224.33 s
|
| 1410 |
+
2026-03-04 08:20:20.718727: Yayy! New best EMA pseudo Dice: 0.46560001373291016
|
| 1411 |
+
2026-03-04 08:20:28.924547:
|
| 1412 |
+
2026-03-04 08:20:28.924691: Epoch 190
|
| 1413 |
+
2026-03-04 08:20:28.924798: Current learning rate: 0.00067
|
| 1414 |
+
2026-03-04 08:24:06.052186: train_loss 0.4028
|
| 1415 |
+
2026-03-04 08:24:06.207957: val_loss 0.4154
|
| 1416 |
+
2026-03-04 08:24:06.209778: Pseudo dice [np.float32(0.2848), np.float32(0.6411)]
|
| 1417 |
+
2026-03-04 08:24:06.213333: Epoch time: 217.11 s
|
| 1418 |
+
2026-03-04 08:24:10.347288:
|
| 1419 |
+
2026-03-04 08:24:10.352792: Epoch 191
|
| 1420 |
+
2026-03-04 08:24:10.352899: Current learning rate: 0.00061
|
| 1421 |
+
2026-03-04 08:27:54.714699: train_loss 0.4307
|
| 1422 |
+
2026-03-04 08:27:54.917128: val_loss 0.4089
|
| 1423 |
+
2026-03-04 08:27:54.920840: Pseudo dice [np.float32(0.2687), np.float32(0.6722)]
|
| 1424 |
+
2026-03-04 08:27:54.985043: Epoch time: 224.23 s
|
| 1425 |
+
2026-03-04 08:27:54.985788: Yayy! New best EMA pseudo Dice: 0.4659000039100647
|
| 1426 |
+
2026-03-04 08:28:04.302573:
|
| 1427 |
+
2026-03-04 08:28:04.302780: Epoch 192
|
| 1428 |
+
2026-03-04 08:28:04.302879: Current learning rate: 0.00055
|
| 1429 |
+
2026-03-04 08:31:38.604707: train_loss 0.4222
|
| 1430 |
+
2026-03-04 08:31:38.742967: val_loss 0.4519
|
| 1431 |
+
2026-03-04 08:31:38.744669: Pseudo dice [np.float32(0.3173), np.float32(0.6223)]
|
| 1432 |
+
2026-03-04 08:31:38.756071: Epoch time: 214.3 s
|
| 1433 |
+
2026-03-04 08:31:38.761677: Yayy! New best EMA pseudo Dice: 0.46619999408721924
|
| 1434 |
+
2026-03-04 08:31:46.410888:
|
| 1435 |
+
2026-03-04 08:31:46.411194: Epoch 193
|
| 1436 |
+
2026-03-04 08:31:46.411294: Current learning rate: 0.00049
|
| 1437 |
+
2026-03-04 08:35:32.006943: train_loss 0.3934
|
| 1438 |
+
2026-03-04 08:35:32.241206: val_loss 0.4015
|
| 1439 |
+
2026-03-04 08:35:32.241328: Pseudo dice [np.float32(0.2354), np.float32(0.7017)]
|
| 1440 |
+
2026-03-04 08:35:32.278858: Epoch time: 225.54 s
|
| 1441 |
+
2026-03-04 08:35:32.281056: Yayy! New best EMA pseudo Dice: 0.46650001406669617
|
| 1442 |
+
2026-03-04 08:35:40.792664:
|
| 1443 |
+
2026-03-04 08:35:40.792816: Epoch 194
|
| 1444 |
+
2026-03-04 08:35:40.792918: Current learning rate: 0.00043
|
| 1445 |
+
2026-03-04 08:39:18.324843: train_loss 0.4072
|
| 1446 |
+
2026-03-04 08:39:18.432404: val_loss 0.4002
|
| 1447 |
+
2026-03-04 08:39:18.434314: Pseudo dice [np.float32(0.2994), np.float32(0.6559)]
|
| 1448 |
+
2026-03-04 08:39:18.450649: Epoch time: 217.5 s
|
| 1449 |
+
2026-03-04 08:39:18.450865: Yayy! New best EMA pseudo Dice: 0.4675999879837036
|
| 1450 |
+
2026-03-04 08:39:26.084370:
|
| 1451 |
+
2026-03-04 08:39:26.084591: Epoch 195
|
| 1452 |
+
2026-03-04 08:39:26.084690: Current learning rate: 0.00036
|
| 1453 |
+
2026-03-04 08:43:13.079995: train_loss 0.3997
|
| 1454 |
+
2026-03-04 08:43:13.312263: val_loss 0.4622
|
| 1455 |
+
2026-03-04 08:43:13.318813: Pseudo dice [np.float32(0.2441), np.float32(0.6523)]
|
| 1456 |
+
2026-03-04 08:43:13.371636: Epoch time: 226.95 s
|
| 1457 |
+
2026-03-04 08:43:18.201359:
|
| 1458 |
+
2026-03-04 08:43:18.203734: Epoch 196
|
| 1459 |
+
2026-03-04 08:43:18.203987: Current learning rate: 0.0003
|
| 1460 |
+
2026-03-04 08:46:49.628490: train_loss 0.426
|
| 1461 |
+
2026-03-04 08:46:49.652349: val_loss 0.4363
|
| 1462 |
+
2026-03-04 08:46:49.652919: Pseudo dice [np.float32(0.2914), np.float32(0.6169)]
|
| 1463 |
+
2026-03-04 08:46:49.654878: Epoch time: 211.43 s
|
| 1464 |
+
2026-03-04 08:46:54.415994:
|
| 1465 |
+
2026-03-04 08:46:54.416220: Epoch 197
|
| 1466 |
+
2026-03-04 08:46:54.416321: Current learning rate: 0.00023
|
| 1467 |
+
2026-03-04 08:50:39.489568: train_loss 0.402
|
| 1468 |
+
2026-03-04 08:50:39.620687: val_loss 0.4078
|
| 1469 |
+
2026-03-04 08:50:39.621533: Pseudo dice [np.float32(0.2493), np.float32(0.6684)]
|
| 1470 |
+
2026-03-04 08:50:39.625008: Epoch time: 225.04 s
|
| 1471 |
+
2026-03-04 08:50:43.913258:
|
| 1472 |
+
2026-03-04 08:50:43.913420: Epoch 198
|
| 1473 |
+
2026-03-04 08:50:43.913516: Current learning rate: 0.00016
|
| 1474 |
+
2026-03-04 08:54:21.828848: train_loss 0.4104
|
| 1475 |
+
2026-03-04 08:54:22.017017: val_loss 0.4411
|
| 1476 |
+
2026-03-04 08:54:22.019436: Pseudo dice [np.float32(0.2934), np.float32(0.6352)]
|
| 1477 |
+
2026-03-04 08:54:22.035548: Epoch time: 217.91 s
|
| 1478 |
+
2026-03-04 08:54:53.174015:
|
| 1479 |
+
2026-03-04 08:54:53.174418: Epoch 199
|
| 1480 |
+
2026-03-04 08:54:53.174554: Current learning rate: 8e-05
|
| 1481 |
+
2026-03-04 08:58:22.628339: train_loss 0.4127
|
| 1482 |
+
2026-03-04 08:58:22.915083: val_loss 0.4274
|
| 1483 |
+
2026-03-04 08:58:22.919220: Pseudo dice [np.float32(0.3037), np.float32(0.6337)]
|
| 1484 |
+
2026-03-04 08:58:22.957631: Epoch time: 209.38 s
|
| 1485 |
+
2026-03-04 08:58:30.639652: Training done.
|
| 1486 |
+
2026-03-04 08:58:31.129257: predicting vesuvius_0000
|
| 1487 |
+
2026-03-04 08:58:31.914719: vesuvius_0000, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1488 |
+
2026-03-04 08:59:33.985875: predicting vesuvius_0001
|
| 1489 |
+
2026-03-04 08:59:34.574161: vesuvius_0001, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1490 |
+
2026-03-04 08:59:51.343027: predicting vesuvius_0002
|
| 1491 |
+
2026-03-04 08:59:51.951585: vesuvius_0002, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1492 |
+
2026-03-04 09:00:08.748374: predicting vesuvius_0003
|
| 1493 |
+
2026-03-04 09:00:09.330821: vesuvius_0003, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1494 |
+
2026-03-04 09:00:26.105629: predicting vesuvius_0004
|
| 1495 |
+
2026-03-04 09:00:26.709286: vesuvius_0004, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1496 |
+
2026-03-04 09:00:43.474800: predicting vesuvius_0005
|
| 1497 |
+
2026-03-04 09:00:43.995079: vesuvius_0005, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1498 |
+
2026-03-04 09:01:00.804692: predicting vesuvius_0006
|
| 1499 |
+
2026-03-04 09:01:01.386648: vesuvius_0006, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1500 |
+
2026-03-04 09:01:18.209996: predicting vesuvius_0007
|
| 1501 |
+
2026-03-04 09:01:18.810788: vesuvius_0007, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1502 |
+
2026-03-04 09:01:35.649444: predicting vesuvius_0008
|
| 1503 |
+
2026-03-04 09:01:36.150120: vesuvius_0008, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1504 |
+
2026-03-04 09:01:52.970459: predicting vesuvius_0009
|
| 1505 |
+
2026-03-04 09:01:53.519801: vesuvius_0009, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1506 |
+
2026-03-04 09:02:10.354175: predicting vesuvius_0010
|
| 1507 |
+
2026-03-04 09:02:10.910054: vesuvius_0010, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1508 |
+
2026-03-04 09:02:27.684789: predicting vesuvius_0011
|
| 1509 |
+
2026-03-04 09:02:28.322778: vesuvius_0011, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1510 |
+
2026-03-04 09:02:45.171811: predicting vesuvius_0012
|
| 1511 |
+
2026-03-04 09:02:45.763647: vesuvius_0012, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1512 |
+
2026-03-04 09:03:02.606927: predicting vesuvius_0013
|
| 1513 |
+
2026-03-04 09:03:03.185725: vesuvius_0013, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1514 |
+
2026-03-04 09:03:20.037134: predicting vesuvius_0014
|
| 1515 |
+
2026-03-04 09:03:20.483872: vesuvius_0014, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1516 |
+
2026-03-04 09:03:37.380350: predicting vesuvius_0015
|
| 1517 |
+
2026-03-04 09:03:37.961883: vesuvius_0015, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1518 |
+
2026-03-04 09:03:54.810737: predicting vesuvius_0016
|
| 1519 |
+
2026-03-04 09:03:55.391566: vesuvius_0016, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1520 |
+
2026-03-04 09:04:12.219199: predicting vesuvius_0017
|
| 1521 |
+
2026-03-04 09:04:12.840497: vesuvius_0017, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1522 |
+
2026-03-04 09:04:29.708312: predicting vesuvius_0018
|
| 1523 |
+
2026-03-04 09:04:30.274498: vesuvius_0018, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1524 |
+
2026-03-04 09:04:47.137577: predicting vesuvius_0019
|
| 1525 |
+
2026-03-04 09:04:47.740392: vesuvius_0019, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1526 |
+
2026-03-04 09:05:04.617381: predicting vesuvius_0020
|
| 1527 |
+
2026-03-04 09:05:05.193587: vesuvius_0020, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1528 |
+
2026-03-04 09:05:22.071307: predicting vesuvius_0021
|
| 1529 |
+
2026-03-04 09:05:22.629648: vesuvius_0021, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1530 |
+
2026-03-04 09:05:39.502246: predicting vesuvius_0022
|
| 1531 |
+
2026-03-04 09:05:40.101514: vesuvius_0022, shape torch.Size([1, 320, 314, 314]), rank 0
|
| 1532 |
+
2026-0
|