Datasets:
ngaggion
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ChronoRoot_nnUNet

Dataset card Data Studio Files
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1.16k
2025-01-20 20:38:40.153602:
2025-01-20 20:38:40.188071: Epoch 378
2025-01-20 20:38:40.188133: Current learning rate: 0.00652
2025-01-20 20:39:27.818059: train_loss -0.7096
2025-01-20 20:39:27.853166: val_loss -0.7196
2025-01-20 20:39:27.853233: Pseudo dice [np.float32(0.7693), np.float32(0.7826), np.float32(0.8619), np.float32(0.7668), np.float32(0.895), np.float32(0.7519)]
2025-01-20 20:39:27.853273: Epoch time: 47.66 s
2025-01-20 20:39:28.440623:
2025-01-20 20:39:28.475080: Epoch 379
2025-01-20 20:39:28.475175: Current learning rate: 0.00651
2025-01-20 20:40:16.132962: train_loss -0.7223
2025-01-20 20:40:16.168122: val_loss -0.7314
2025-01-20 20:40:16.168175: Pseudo dice [np.float32(0.7658), np.float32(0.7601), np.float32(0.8666), np.float32(0.768), np.float32(0.8975), np.float32(0.7758)]
2025-01-20 20:40:16.168212: Epoch time: 47.69 s
2025-01-20 20:40:16.640620:
2025-01-20 20:40:16.675080: Epoch 380
2025-01-20 20:40:16.675141: Current learning rate: 0.0065
2025-01-20 20:41:04.363595: train_loss -0.7314
2025-01-20 20:41:04.398608: val_loss -0.7262
2025-01-20 20:41:04.398696: Pseudo dice [np.float32(0.7715), np.float32(0.7873), np.float32(0.8624), np.float32(0.7842), np.float32(0.8937), np.float32(0.8206)]
2025-01-20 20:41:04.398741: Epoch time: 47.72 s
2025-01-20 20:41:04.398763: Yayy! New best EMA pseudo Dice: 0.8041999936103821
2025-01-20 20:41:05.255918:
2025-01-20 20:41:05.256258: Epoch 381
2025-01-20 20:41:05.256315: Current learning rate: 0.00649
2025-01-20 20:41:52.890304: train_loss -0.719
2025-01-20 20:41:52.925525: val_loss -0.7276
2025-01-20 20:41:52.925595: Pseudo dice [np.float32(0.7725), np.float32(0.7758), np.float32(0.8632), np.float32(0.7394), np.float32(0.8952), np.float32(0.7956)]
2025-01-20 20:41:52.925632: Epoch time: 47.63 s
2025-01-20 20:41:52.925657: Yayy! New best EMA pseudo Dice: 0.8044999837875366
2025-01-20 20:41:53.789770:
2025-01-20 20:41:53.792254: Epoch 382
2025-01-20 20:41:53.792345: Current learning rate: 0.00648
2025-01-20 20:42:41.406392: train_loss -0.7213
2025-01-20 20:42:41.441584: val_loss -0.7181
2025-01-20 20:42:41.441641: Pseudo dice [np.float32(0.7692), np.float32(0.7882), np.float32(0.8614), np.float32(0.7433), np.float32(0.8908), np.float32(0.7776)]
2025-01-20 20:42:41.441677: Epoch time: 47.62 s
2025-01-20 20:42:41.441698: Yayy! New best EMA pseudo Dice: 0.8046000003814697
2025-01-20 20:42:42.300387:
2025-01-20 20:42:42.335733: Epoch 383
2025-01-20 20:42:42.335823: Current learning rate: 0.00648
2025-01-20 20:43:30.036393: train_loss -0.7378
2025-01-20 20:43:30.071504: val_loss -0.7206
2025-01-20 20:43:30.071564: Pseudo dice [np.float32(0.7645), np.float32(0.7717), np.float32(0.8588), np.float32(0.7605), np.float32(0.896), np.float32(0.7926)]
2025-01-20 20:43:30.071601: Epoch time: 47.74 s
2025-01-20 20:43:30.071621: Yayy! New best EMA pseudo Dice: 0.8047999739646912
2025-01-20 20:43:30.927972:
2025-01-20 20:43:30.963286: Epoch 384
2025-01-20 20:43:30.963386: Current learning rate: 0.00647
2025-01-20 20:44:18.594276: train_loss -0.7225
2025-01-20 20:44:18.629380: val_loss -0.7032
2025-01-20 20:44:18.629433: Pseudo dice [np.float32(0.7545), np.float32(0.7339), np.float32(0.8576), np.float32(0.7748), np.float32(0.8799), np.float32(0.7799)]
2025-01-20 20:44:18.629470: Epoch time: 47.67 s
2025-01-20 20:44:19.099324:
2025-01-20 20:44:19.133787: Epoch 385
2025-01-20 20:44:19.133863: Current learning rate: 0.00646
2025-01-20 20:45:06.756201: train_loss -0.7235
2025-01-20 20:45:06.791202: val_loss -0.697
2025-01-20 20:45:06.791274: Pseudo dice [np.float32(0.7696), np.float32(0.7681), np.float32(0.8636), np.float32(0.7618), np.float32(0.8977), np.float32(0.8029)]
2025-01-20 20:45:06.791310: Epoch time: 47.66 s
2025-01-20 20:45:07.368687:
2025-01-20 20:45:07.403075: Epoch 386
2025-01-20 20:45:07.403187: Current learning rate: 0.00645
2025-01-20 20:45:55.070118: train_loss -0.7325
2025-01-20 20:45:55.105150: val_loss -0.715
2025-01-20 20:45:55.105227: Pseudo dice [np.float32(0.7593), np.float32(0.7613), np.float32(0.8626), np.float32(0.7663), np.float32(0.8926), np.float32(0.7798)]
2025-01-20 20:45:55.105261: Epoch time: 47.7 s
2025-01-20 20:45:55.574217:
2025-01-20 20:45:55.608675: Epoch 387
2025-01-20 20:45:55.608780: Current learning rate: 0.00644
2025-01-20 20:46:43.277921: train_loss -0.7275
2025-01-20 20:46:43.313028: val_loss -0.6998
2025-01-20 20:46:43.313082: Pseudo dice [np.float32(0.7461), np.float32(0.7692), np.float32(0.858), np.float32(0.7139), np.float32(0.8945), np.float32(0.7774)]
2025-01-20 20:46:43.313126: Epoch time: 47.7 s
2025-01-20 20:46:43.795102:
2025-01-20 20:46:43.829448: Epoch 388
2025-01-20 20:46:43.829521: Current learning rate: 0.00643
2025-01-20 20:47:31.491718: train_loss -0.7237
2025-01-20 20:47:31.526831: val_loss -0.711
2025-01-20 20:47:31.526885: Pseudo dice [np.float32(0.7558), np.float32(0.7508), np.float32(0.8662), np.float32(0.75), np.float32(0.8913), np.float32(0.7857)]
2025-01-20 20:47:31.526920: Epoch time: 47.7 s
2025-01-20 20:47:31.997616:
2025-01-20 20:47:32.032078: Epoch 389
2025-01-20 20:47:32.032139: Current learning rate: 0.00642
2025-01-20 20:48:19.744259: train_loss -0.7313
2025-01-20 20:48:19.779379: val_loss -0.7113
2025-01-20 20:48:19.779438: Pseudo dice [np.float32(0.7582), np.float32(0.7892), np.float32(0.8684), np.float32(0.7615), np.float32(0.885), np.float32(0.772)]
2025-01-20 20:48:19.779478: Epoch time: 47.75 s
2025-01-20 20:48:20.249680:
2025-01-20 20:48:20.284134: Epoch 390
2025-01-20 20:48:20.284196: Current learning rate: 0.00641
2025-01-20 20:49:08.001420: train_loss -0.7368
2025-01-20 20:49:08.036478: val_loss -0.7178
2025-01-20 20:49:08.036572: Pseudo dice [np.float32(0.7614), np.float32(0.7866), np.float32(0.8661), np.float32(0.7572), np.float32(0.9044), np.float32(0.7928)]
2025-01-20 20:49:08.036608: Epoch time: 47.75 s
2025-01-20 20:49:08.506727:
2025-01-20 20:49:08.541202: Epoch 391
2025-01-20 20:49:08.541263: Current learning rate: 0.0064
2025-01-20 20:49:56.218514: train_loss -0.7196
2025-01-20 20:49:56.275237: val_loss -0.7269