Datasets:
ngaggion
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ChronoRoot_nnUNet

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1.16k
2025-01-20 16:22:13.029695: Epoch time: 47.77 s
2025-01-20 16:22:13.029729: Yayy! New best EMA pseudo Dice: 0.7695000171661377
2025-01-20 16:22:13.876381:
2025-01-20 16:22:13.880178: Epoch 60
2025-01-20 16:22:13.880282: Current learning rate: 0.00946
2025-01-20 16:23:01.628436: train_loss -0.6713
2025-01-20 16:23:01.663608: val_loss -0.6823
2025-01-20 16:23:01.663681: Pseudo dice [np.float32(0.7397), np.float32(0.7652), np.float32(0.8347), np.float32(0.7251), np.float32(0.8776), np.float32(0.7531)]
2025-01-20 16:23:01.663739: Epoch time: 47.75 s
2025-01-20 16:23:01.663783: Yayy! New best EMA pseudo Dice: 0.770799994468689
2025-01-20 16:23:02.511387:
2025-01-20 16:23:02.546733: Epoch 61
2025-01-20 16:23:02.546809: Current learning rate: 0.00945
2025-01-20 16:23:50.351604: train_loss -0.6834
2025-01-20 16:23:50.351718: val_loss -0.6913
2025-01-20 16:23:50.351765: Pseudo dice [np.float32(0.7507), np.float32(0.7435), np.float32(0.8514), np.float32(0.716), np.float32(0.8775), np.float32(0.7592)]
2025-01-20 16:23:50.351820: Epoch time: 47.84 s
2025-01-20 16:23:50.351841: Yayy! New best EMA pseudo Dice: 0.7720000147819519
2025-01-20 16:23:51.140569:
2025-01-20 16:23:51.143241: Epoch 62
2025-01-20 16:23:51.143347: Current learning rate: 0.00944
2025-01-20 16:24:38.924830: train_loss -0.6766
2025-01-20 16:24:38.959991: val_loss -0.6782
2025-01-20 16:24:38.960059: Pseudo dice [np.float32(0.737), np.float32(0.7639), np.float32(0.8427), np.float32(0.6739), np.float32(0.8784), np.float32(0.7523)]
2025-01-20 16:24:38.960098: Epoch time: 47.78 s
2025-01-20 16:24:38.960119: Yayy! New best EMA pseudo Dice: 0.7723000049591064
2025-01-20 16:24:39.808050:
2025-01-20 16:24:39.808105: Epoch 63
2025-01-20 16:24:39.808177: Current learning rate: 0.00943
2025-01-20 16:25:27.606040: train_loss -0.6897
2025-01-20 16:25:27.606212: val_loss -0.6773
2025-01-20 16:25:27.606255: Pseudo dice [np.float32(0.7342), np.float32(0.7476), np.float32(0.8453), np.float32(0.7528), np.float32(0.8714), np.float32(0.7598)]
2025-01-20 16:25:27.606294: Epoch time: 47.8 s
2025-01-20 16:25:27.606316: Yayy! New best EMA pseudo Dice: 0.7735999822616577
2025-01-20 16:25:28.454947:
2025-01-20 16:25:28.458085: Epoch 64
2025-01-20 16:25:28.458176: Current learning rate: 0.00942
2025-01-20 16:26:16.237535: train_loss -0.6846
2025-01-20 16:26:16.272680: val_loss -0.6932
2025-01-20 16:26:16.272751: Pseudo dice [np.float32(0.7569), np.float32(0.7704), np.float32(0.8499), np.float32(0.7307), np.float32(0.874), np.float32(0.7562)]
2025-01-20 16:26:16.272788: Epoch time: 47.78 s
2025-01-20 16:26:16.272808: Yayy! New best EMA pseudo Dice: 0.7752000093460083
2025-01-20 16:26:17.121657:
2025-01-20 16:26:17.156901: Epoch 65
2025-01-20 16:26:17.156976: Current learning rate: 0.00941
2025-01-20 16:27:04.932352: train_loss -0.684
2025-01-20 16:27:04.967464: val_loss -0.676
2025-01-20 16:27:04.967519: Pseudo dice [np.float32(0.7412), np.float32(0.7781), np.float32(0.8441), np.float32(0.7063), np.float32(0.8766), np.float32(0.7573)]
2025-01-20 16:27:04.967554: Epoch time: 47.81 s
2025-01-20 16:27:04.967577: Yayy! New best EMA pseudo Dice: 0.7760999798774719
2025-01-20 16:27:05.898137:
2025-01-20 16:27:05.902396: Epoch 66
2025-01-20 16:27:05.902498: Current learning rate: 0.0094
2025-01-20 16:27:53.664447: train_loss -0.6787
2025-01-20 16:27:53.699466: val_loss -0.6554
2025-01-20 16:27:53.699533: Pseudo dice [np.float32(0.7318), np.float32(0.7195), np.float32(0.847), np.float32(0.6773), np.float32(0.8777), np.float32(0.755)]
2025-01-20 16:27:53.699574: Epoch time: 47.77 s
2025-01-20 16:27:54.160348:
2025-01-20 16:27:54.194685: Epoch 67
2025-01-20 16:27:54.194761: Current learning rate: 0.00939
2025-01-20 16:28:41.984346: train_loss -0.6731
2025-01-20 16:28:41.984526: val_loss -0.6775
2025-01-20 16:28:41.984594: Pseudo dice [np.float32(0.7323), np.float32(0.7427), np.float32(0.8474), np.float32(0.7376), np.float32(0.8835), np.float32(0.7603)]
2025-01-20 16:28:41.984632: Epoch time: 47.82 s
2025-01-20 16:28:41.984653: Yayy! New best EMA pseudo Dice: 0.7760999798774719
2025-01-20 16:28:42.838468:
2025-01-20 16:28:42.838656: Epoch 68
2025-01-20 16:28:42.838710: Current learning rate: 0.00939
2025-01-20 16:29:30.629951: train_loss -0.6798
2025-01-20 16:29:30.665121: val_loss -0.6763
2025-01-20 16:29:30.665203: Pseudo dice [np.float32(0.7437), np.float32(0.7451), np.float32(0.8359), np.float32(0.7331), np.float32(0.8604), np.float32(0.7558)]
2025-01-20 16:29:30.665240: Epoch time: 47.79 s
2025-01-20 16:29:30.665262: Yayy! New best EMA pseudo Dice: 0.7764000296592712
2025-01-20 16:29:31.512890:
2025-01-20 16:29:31.548116: Epoch 69
2025-01-20 16:29:31.548211: Current learning rate: 0.00938
2025-01-20 16:30:19.321309: train_loss -0.6804
2025-01-20 16:30:19.380729: val_loss -0.6492
2025-01-20 16:30:19.380786: Pseudo dice [np.float32(0.7337), np.float32(0.718), np.float32(0.8397), np.float32(0.7069), np.float32(0.8572), np.float32(0.7324)]
2025-01-20 16:30:19.380822: Epoch time: 47.81 s
2025-01-20 16:30:19.846573:
2025-01-20 16:30:19.880946: Epoch 70
2025-01-20 16:30:19.881008: Current learning rate: 0.00937
2025-01-20 16:31:07.669622: train_loss -0.6748
2025-01-20 16:31:07.704755: val_loss -0.677
2025-01-20 16:31:07.704814: Pseudo dice [np.float32(0.7456), np.float32(0.7505), np.float32(0.8363), np.float32(0.7328), np.float32(0.8665), np.float32(0.742)]
2025-01-20 16:31:07.704851: Epoch time: 47.82 s
2025-01-20 16:31:08.170349:
2025-01-20 16:31:08.170404: Epoch 71
2025-01-20 16:31:08.170492: Current learning rate: 0.00936
2025-01-20 16:31:55.942519: train_loss -0.692
2025-01-20 16:31:55.942620: val_loss -0.7014
2025-01-20 16:31:55.942678: Pseudo dice [np.float32(0.7495), np.float32(0.7598), np.float32(0.8437), np.float32(0.7027), np.float32(0.8749), np.float32(0.7707)]
2025-01-20 16:31:55.942727: Epoch time: 47.77 s
2025-01-20 16:31:56.410116:
2025-01-20 16:31:56.444568: Epoch 72
2025-01-20 16:31:56.444641: Current learning rate: 0.00935
2025-01-20 16:32:44.228125: train_loss -0.6846
2025-01-20 16:32:44.263208: val_loss -0.6783
2025-01-20 16:32:44.263277: Pseudo dice [np.float32(0.7388), np.float32(0.7359), np.float32(0.8465), np.float32(0.7022), np.float32(0.8668), np.float32(0.7704)]