log-08-29Normal.txt

PreRun: Creating required directories
Directory Outputs/ already exists
Directory log/ already exists
PreRun: generating experiment config file
Using cuda device
configuring data loaders
configuring model - EluNet
saving model summary
logging parameters
starting training

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train_loss: 0.3301415029499266
train_mIoU: 0.7233797896721503
train_accuracy: 0.8526197160993304

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val_loss: 0.44180699549223246
val_mIoU: 0.41430436462599207
val_accuracy: 0.721535997892681
saving model

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train_loss: 0.1880066692829132
train_mIoU: 0.7668230684563249
train_accuracy: 0.8787573428008819

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val_loss: 0.3057150273574026
val_mIoU: 0.561612080984308
val_accuracy: 0.7224020225123355
saving model

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train_loss: 0.15080827253836174
train_mIoU: 0.7850625735670189
train_accuracy: 0.8909424328413166

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val_loss: 0.32704703054930034
val_mIoU: 0.4891370690470195
val_accuracy: 0.6574141010485197

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train_loss: 0.1366737943004679
train_mIoU: 0.7935972100428699
train_accuracy: 0.8962447012336249

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Epoch 3/50: 100%|██████████| 475/475 [00:10<00:00, 43.76it/s]
val_loss: 0.26752577681290474
val_mIoU: 0.5524551061522217
val_accuracy: 0.7146669729132401

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Epoch 4/50: 100%|██████████| 27/27 [00:07<00:00,  3.53it/s]
train_loss: 0.13077246370138945
train_mIoU: 0.7979706153488195
train_accuracy: 0.8990030757716445

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Epoch 4/50: 100%|██████████| 475/475 [00:10<00:00, 45.30it/s]
val_loss: 0.258825405208688
val_mIoU: 0.554030286006302
val_accuracy: 0.7156067858244244

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Epoch 5/50: 100%|██████████| 27/27 [00:08<00:00,  3.31it/s]
train_loss: 0.12753925362118967
train_mIoU: 0.7994592815537336
train_accuracy: 0.8999096693981448

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val_loss: 0.2996062769387898
val_mIoU: 0.5044266615362711
val_accuracy: 0.6723933169716283

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train_loss: 0.12432914199652495
train_mIoU: 0.8022122422486233
train_accuracy: 0.9015479344673961

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Epoch 6/50: 100%|██████████| 475/475 [00:10<00:00, 43.80it/s]
val_loss: 0.2411277046956514
val_mIoU: 0.5816777695682426
val_accuracy: 0.7407714522512335
saving model

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train_loss: 0.12149924370977613
train_mIoU: 0.8055540364565092
train_accuracy: 0.9035161038472446

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Epoch 7/50: 100%|██████████| 475/475 [00:10<00:00, 45.87it/s]
val_loss: 0.2846522721491362
val_mIoU: 0.5296549713016547
val_accuracy: 0.6963927258943257

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Epoch 8/50:  81%|████████▏ | 22/27 [00:06<00:01,  2.87it/s]
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Epoch 8/50:  89%|████████▉ | 24/27 [00:07<00:00,  3.51it/s]
Epoch 8/50:  93%|█████████▎| 25/27 [00:07<00:00,  3.34it/s]
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Epoch 8/50: 100%|██████████| 27/27 [00:08<00:00,  3.34it/s]
train_loss: 0.12001719656917784
train_mIoU: 0.8072794429036286
train_accuracy: 0.9045441602934718

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Epoch 8/50:  90%|█████████ | 428/475 [00:09<00:00, 52.11it/s]
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Epoch 8/50:  93%|█████████▎| 444/475 [00:09<00:00, 39.18it/s]
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Epoch 8/50:  96%|█████████▌| 455/475 [00:09<00:00, 41.42it/s]
Epoch 8/50:  97%|█████████▋| 460/475 [00:09<00:00, 40.24it/s]
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Epoch 8/50: 100%|██████████| 475/475 [00:10<00:00, 46.20it/s]
val_loss: 0.2795346503508718
val_mIoU: 0.5561515986537722
val_accuracy: 0.7261305638363487

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train_loss: 0.11781152061842105
train_mIoU: 0.8096644252730837
train_accuracy: 0.9058743193222153

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val_loss: 0.27715440298381605
val_mIoU: 0.5941194404466416
val_accuracy: 0.7678134637129934
saving model

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train_loss: 0.11667270296149784
train_mIoU: 0.8113683362753757
train_accuracy: 0.9068904045873281

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Epoch 10/50:  96%|█████████▌| 457/475 [00:10<00:00, 42.48it/s]
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Epoch 10/50:  99%|█████████▊| 469/475 [00:10<00:00, 48.81it/s]
Epoch 10/50: 100%|██████████| 475/475 [00:10<00:00, 44.61it/s]
val_loss: 0.27141242127669485
val_mIoU: 0.5275437964461589
val_accuracy: 0.6924466745476974

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Epoch 11/50: 100%|██████████| 27/27 [00:07<00:00,  3.47it/s]
train_loss: 0.1148294734734076
train_mIoU: 0.8134943991104071
train_accuracy: 0.9080520379738729

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Epoch 11/50:  77%|███████▋  | 364/475 [00:08<00:02, 41.28it/s]
Epoch 11/50:  78%|███████▊  | 369/475 [00:08<00:02, 39.21it/s]
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Epoch 11/50:  80%|████████  | 380/475 [00:08<00:02, 43.83it/s]
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Epoch 11/50:  82%|████████▏ | 390/475 [00:08<00:02, 42.37it/s]
Epoch 11/50:  83%|████████▎ | 395/475 [00:08<00:02, 38.73it/s]
Epoch 11/50:  84%|████████▍ | 400/475 [00:09<00:01, 41.19it/s]
Epoch 11/50:  86%|████████▌ | 407/475 [00:09<00:01, 47.51it/s]
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Epoch 11/50:  88%|████████▊ | 419/475 [00:09<00:01, 52.78it/s]
Epoch 11/50:  89%|████████▉ | 425/475 [00:09<00:00, 50.61it/s]
Epoch 11/50:  91%|█████████ | 431/475 [00:09<00:00, 44.75it/s]
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Epoch 11/50:  96%|█████████▌| 455/475 [00:10<00:00, 48.99it/s]
Epoch 11/50:  97%|█████████▋| 461/475 [00:10<00:00, 41.79it/s]
Epoch 11/50:  98%|█████████▊| 467/475 [00:10<00:00, 45.07it/s]
Epoch 11/50: 100%|█████████▉| 474/475 [00:10<00:00, 49.61it/s]
Epoch 11/50: 100%|██████████| 475/475 [00:10<00:00, 44.73it/s]
val_loss: 0.26560483775640786
val_mIoU: 0.5859875082080468
val_accuracy: 0.7538805831106086

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Epoch 12/50:  78%|███████▊  | 21/27 [00:06<00:01,  3.22it/s]
Epoch 12/50:  81%|████████▏ | 22/27 [00:06<00:01,  3.53it/s]
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Epoch 12/50:  89%|████████▉ | 24/27 [00:07<00:00,  3.51it/s]
Epoch 12/50:  93%|█████████▎| 25/27 [00:07<00:00,  3.61it/s]
Epoch 12/50:  96%|█████████▋| 26/27 [00:07<00:00,  3.45it/s]
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Epoch 12/50: 100%|██████████| 27/27 [00:07<00:00,  3.47it/s]
train_loss: 0.11345140801535712
train_mIoU: 0.8153685691956669
train_accuracy: 0.9091017140158446

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val_loss: 0.3101079472115165
val_mIoU: 0.4892924817894451
val_accuracy: 0.6576390316611842

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train_loss: 0.11529048642626515
train_mIoU: 0.8128492530830131
train_accuracy: 0.9077784160819489

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Epoch 13/50:  97%|█████████▋| 461/475 [00:09<00:00, 48.69it/s]
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Epoch 13/50: 100%|█████████▉| 474/475 [00:10<00:00, 49.53it/s]
Epoch 13/50: 100%|██████████| 475/475 [00:10<00:00, 46.74it/s]
val_loss: 0.2312528707479176
val_mIoU: 0.6002429849369252
val_accuracy: 0.7576507568359375

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Epoch 14/50: 100%|██████████| 27/27 [00:08<00:00,  3.35it/s]
train_loss: 0.1109235926910683
train_mIoU: 0.818935282912469
train_accuracy: 0.9111106926160897

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val_loss: 0.28798673278407044
val_mIoU: 0.5889679120806754
val_accuracy: 0.7683175498560856
saving model

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train_loss: 0.11067708554091277
train_mIoU: 0.8197006701263014
train_accuracy: 0.9116116954794533

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val_loss: 0.5506856464084826
val_mIoU: 0.2703513470866591
val_accuracy: 0.43745730751439144

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train_loss: 0.1272255861096912
train_mIoU: 0.7962689911372938
train_accuracy: 0.8985445974023895

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Epoch 16/50: 100%|██████████| 475/475 [00:10<00:00, 47.19it/s]
val_loss: 0.33529790589683933
val_mIoU: 0.45661058656792175
val_accuracy: 0.6270086348684211

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train_loss: 0.12128104379883518
train_mIoU: 0.8049244278366539
train_accuracy: 0.9034467424665179

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Epoch 17/50: 100%|██████████| 475/475 [00:10<00:00, 45.37it/s]
val_loss: 0.23800227893026252
val_mIoU: 0.6111548248442136
val_accuracy: 0.769545255962171
saving model

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Epoch 18/50: 100%|██████████| 27/27 [00:07<00:00,  3.46it/s]
train_loss: 0.11686536090241538
train_mIoU: 0.8107570536876862
train_accuracy: 0.9066313625219555

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Epoch 18/50: 100%|██████████| 475/475 [00:10<00:00, 44.25it/s]
val_loss: 0.28368636419898585
val_mIoU: 0.546402694444222
val_accuracy: 0.7139726819490132

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Epoch 19/50:  67%|██████▋   | 18/27 [00:05<00:02,  3.33it/s]
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Epoch 19/50:  78%|███████▊  | 21/27 [00:06<00:01,  3.41it/s]
Epoch 19/50:  81%|████████▏ | 22/27 [00:06<00:01,  3.46it/s]
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Epoch 19/50:  93%|█████████▎| 25/27 [00:07<00:00,  3.71it/s]
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Epoch 19/50: 100%|██████████| 27/27 [00:08<00:00,  3.31it/s]
train_loss: 0.11511045252835309
train_mIoU: 0.8137663461015388
train_accuracy: 0.9083687918526786

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Epoch 19/50: 100%|██████████| 475/475 [00:10<00:00, 45.22it/s]
val_loss: 0.3036434531211853
val_mIoU: 0.49932414612043374
val_accuracy: 0.6672197522615132

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train_loss: 0.11346582047365329
train_mIoU: 0.816228998606175
train_accuracy: 0.9097483722052474

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val_loss: 0.2182037625814739
val_mIoU: 0.6361442517621705
val_accuracy: 0.7885969302528782
saving model

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train_loss: 0.11191212109945438
train_mIoU: 0.8184989473872966
train_accuracy: 0.9110196759047497

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val_loss: 0.2675915008469632
val_mIoU: 0.6255583372762926
val_accuracy: 0.7967106548108552
saving model

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train_loss: 0.11295832086492467
train_mIoU: 0.8173385313667214
train_accuracy: 0.9104098603652847

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val_loss: 0.2744531500339508
val_mIoU: 0.6086239786185527
val_accuracy: 0.7822933799342106

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train_loss: 0.1107333360446824
train_mIoU: 0.8202981311391622
train_accuracy: 0.9119947732751208

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Epoch 23/50: 100%|██████████| 475/475 [00:11<00:00, 42.19it/s]
val_loss: 0.24783630722447444
val_mIoU: 0.5876099326367819
val_accuracy: 0.748009386564556

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Epoch 24/50: 100%|██████████| 27/27 [00:08<00:00,  3.31it/s]
train_loss: 0.11195845791587124
train_mIoU: 0.8187369279070011
train_accuracy: 0.911222578490925

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Epoch 24/50: 100%|██████████| 475/475 [00:10<00:00, 45.24it/s]
val_loss: 0.26804324695938514
val_mIoU: 0.5434050228231853
val_accuracy: 0.7071602590460526

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Epoch 25/50: 100%|██████████| 27/27 [00:07<00:00,  3.39it/s]
train_loss: 0.10894035934298127
train_mIoU: 0.8230263561607198
train_accuracy: 0.9135216527577027

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Epoch 25/50:  42%|████▏     | 199/475 [00:04<00:05, 46.13it/s]
Epoch 25/50:  43%|████▎     | 205/475 [00:05<00:05, 49.17it/s]
Epoch 25/50:  44%|████▍     | 211/475 [00:05<00:05, 46.95it/s]
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Epoch 25/50:  48%|████▊     | 228/475 [00:05<00:05, 48.62it/s]
Epoch 25/50:  49%|████▉     | 235/475 [00:05<00:04, 53.68it/s]
Epoch 25/50:  51%|█████     | 242/475 [00:05<00:04, 54.33it/s]
Epoch 25/50:  52%|█████▏    | 248/475 [00:05<00:04, 48.70it/s]
Epoch 25/50:  53%|█████▎    | 254/475 [00:06<00:04, 46.77it/s]
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Epoch 25/50:  56%|█████▌    | 266/475 [00:06<00:04, 48.68it/s]
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Epoch 25/50:  59%|█████▉    | 281/475 [00:06<00:04, 44.14it/s]
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Epoch 25/50:  61%|██████▏   | 292/475 [00:06<00:03, 46.30it/s]
Epoch 25/50:  63%|██████▎   | 298/475 [00:07<00:03, 48.40it/s]
Epoch 25/50:  64%|██████▍   | 303/475 [00:07<00:03, 46.34it/s]
Epoch 25/50:  65%|██████▍   | 308/475 [00:07<00:03, 42.65it/s]
Epoch 25/50:  66%|██████▌   | 313/475 [00:07<00:03, 42.12it/s]
Epoch 25/50:  67%|██████▋   | 318/475 [00:07<00:03, 43.71it/s]
Epoch 25/50:  68%|██████▊   | 324/475 [00:07<00:03, 48.05it/s]
Epoch 25/50:  69%|██████▉   | 329/475 [00:07<00:03, 46.70it/s]
Epoch 25/50:  70%|███████   | 334/475 [00:07<00:03, 44.06it/s]
Epoch 25/50:  71%|███████▏  | 339/475 [00:08<00:03, 42.82it/s]
Epoch 25/50:  73%|███████▎  | 345/475 [00:08<00:02, 46.55it/s]
Epoch 25/50:  74%|███████▍  | 351/475 [00:08<00:02, 49.32it/s]
Epoch 25/50:  75%|███████▍  | 356/475 [00:08<00:02, 48.15it/s]
Epoch 25/50:  76%|███████▌  | 361/475 [00:08<00:02, 40.04it/s]
Epoch 25/50:  77%|███████▋  | 366/475 [00:08<00:02, 40.41it/s]
Epoch 25/50:  78%|███████▊  | 372/475 [00:08<00:02, 44.14it/s]
Epoch 25/50:  80%|███████▉  | 378/475 [00:08<00:02, 47.13it/s]
Epoch 25/50:  81%|████████  | 385/475 [00:08<00:01, 49.09it/s]
Epoch 25/50:  82%|████████▏ | 390/475 [00:09<00:01, 48.19it/s]
Epoch 25/50:  83%|████████▎ | 395/475 [00:09<00:01, 43.84it/s]
Epoch 25/50:  84%|████████▍ | 400/475 [00:09<00:01, 44.35it/s]
Epoch 25/50:  85%|████████▌ | 405/475 [00:09<00:01, 45.66it/s]
Epoch 25/50:  86%|████████▋ | 410/475 [00:09<00:01, 41.56it/s]
Epoch 25/50:  88%|████████▊ | 416/475 [00:09<00:01, 45.79it/s]
Epoch 25/50:  89%|████████▊ | 421/475 [00:09<00:01, 46.78it/s]
Epoch 25/50:  90%|████████▉ | 426/475 [00:09<00:01, 44.72it/s]
Epoch 25/50:  91%|█████████ | 431/475 [00:10<00:01, 43.01it/s]
Epoch 25/50:  92%|█████████▏| 437/475 [00:10<00:00, 45.83it/s]
Epoch 25/50:  93%|█████████▎| 443/475 [00:10<00:00, 47.45it/s]
Epoch 25/50:  94%|█████████▍| 448/475 [00:10<00:00, 46.22it/s]
Epoch 25/50:  95%|█████████▌| 453/475 [00:10<00:00, 46.85it/s]
Epoch 25/50:  96%|█████████▋| 458/475 [00:10<00:00, 44.84it/s]
Epoch 25/50:  97%|█████████▋| 463/475 [00:10<00:00, 41.81it/s]
Epoch 25/50:  99%|█████████▊| 468/475 [00:10<00:00, 37.51it/s]
Epoch 25/50: 100%|█████████▉| 473/475 [00:11<00:00, 39.11it/s]
Epoch 25/50: 100%|██████████| 475/475 [00:11<00:00, 42.61it/s]
val_loss: 0.27171101030550504
val_mIoU: 0.6208278073843727
val_accuracy: 0.7930457827919408

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Epoch 26/50:   0%|          | 0/27 [00:00<?, ?it/s]
Epoch 26/50:   4%|▎         | 1/27 [00:00<00:08,  2.90it/s]
Epoch 26/50:   7%|▋         | 2/27 [00:00<00:09,  2.63it/s]
Epoch 26/50:  11%|█         | 3/27 [00:00<00:07,  3.21it/s]
Epoch 26/50:  15%|█▍        | 4/27 [00:01<00:06,  3.54it/s]
Epoch 26/50:  19%|█▊        | 5/27 [00:01<00:06,  3.34it/s]
Epoch 26/50:  22%|██▏       | 6/27 [00:01<00:05,  3.65it/s]
Epoch 26/50:  26%|██▌       | 7/27 [00:02<00:05,  3.35it/s]
Epoch 26/50:  30%|██▉       | 8/27 [00:02<00:05,  3.68it/s]
Epoch 26/50:  33%|███▎      | 9/27 [00:02<00:05,  3.04it/s]
Epoch 26/50:  37%|███▋      | 10/27 [00:03<00:05,  3.37it/s]
Epoch 26/50:  41%|████      | 11/27 [00:03<00:04,  3.40it/s]
Epoch 26/50:  44%|████▍     | 12/27 [00:03<00:04,  3.50it/s]
Epoch 26/50:  48%|████▊     | 13/27 [00:03<00:03,  3.72it/s]
Epoch 26/50:  52%|█████▏    | 14/27 [00:04<00:03,  3.31it/s]
Epoch 26/50:  56%|█████▌    | 15/27 [00:04<00:03,  3.54it/s]
Epoch 26/50:  59%|█████▉    | 16/27 [00:04<00:03,  3.30it/s]
Epoch 26/50:  63%|██████▎   | 17/27 [00:05<00:03,  3.13it/s]
Epoch 26/50:  67%|██████▋   | 18/27 [00:05<00:03,  3.00it/s]
Epoch 26/50:  70%|███████   | 19/27 [00:05<00:02,  2.93it/s]
Epoch 26/50:  74%|███████▍  | 20/27 [00:06<00:02,  2.84it/s]
Epoch 26/50:  78%|███████▊  | 21/27 [00:06<00:02,  2.84it/s]
Epoch 26/50:  81%|████████▏ | 22/27 [00:06<00:01,  2.72it/s]
Epoch 26/50:  85%|████████▌ | 23/27 [00:07<00:01,  2.77it/s]
Epoch 26/50:  89%|████████▉ | 24/27 [00:07<00:01,  2.77it/s]
Epoch 26/50:  93%|█████████▎| 25/27 [00:08<00:00,  2.70it/s]
Epoch 26/50:  96%|█████████▋| 26/27 [00:08<00:00,  2.64it/s]
Epoch 26/50: 100%|██████████| 27/27 [00:08<00:00,  2.73it/s]
Epoch 26/50: 100%|██████████| 27/27 [00:08<00:00,  3.06it/s]
train_loss: 0.10957023473801436
train_mIoU: 0.8221613044576446
train_accuracy: 0.9130598867804999

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Epoch 26/50:   0%|          | 0/475 [00:00<?, ?it/s]
Epoch 26/50:   0%|          | 1/475 [00:00<02:35,  3.04it/s]
Epoch 26/50:   1%|▏         | 6/475 [00:00<00:28, 16.39it/s]
Epoch 26/50:   2%|▏         | 11/475 [00:00<00:18, 25.25it/s]
Epoch 26/50:   3%|▎         | 15/475 [00:00<00:15, 29.16it/s]
Epoch 26/50:   4%|▍         | 20/475 [00:00<00:13, 34.88it/s]
Epoch 26/50:   5%|▌         | 25/475 [00:00<00:13, 34.13it/s]
Epoch 26/50:   6%|▌         | 29/475 [00:01<00:13, 33.23it/s]
Epoch 26/50:   7%|▋         | 33/475 [00:01<00:14, 30.26it/s]
Epoch 26/50:   8%|▊         | 38/475 [00:01<00:12, 33.62it/s]
Epoch 26/50:   9%|▉         | 42/475 [00:01<00:13, 33.29it/s]
Epoch 26/50:  10%|▉         | 46/475 [00:01<00:12, 34.03it/s]
Epoch 26/50:  11%|█         | 50/475 [00:01<00:12, 34.20it/s]
Epoch 26/50:  12%|█▏        | 55/475 [00:01<00:11, 37.06it/s]
Epoch 26/50:  12%|█▏        | 59/475 [00:01<00:11, 35.51it/s]
Epoch 26/50:  13%|█▎        | 64/475 [00:02<00:11, 35.32it/s]
Epoch 26/50:  14%|█▍        | 68/475 [00:02<00:11, 34.30it/s]
Epoch 26/50:  15%|█▌        | 72/475 [00:02<00:11, 33.84it/s]
Epoch 26/50:  16%|█▌        | 77/475 [00:02<00:10, 36.27it/s]
Epoch 26/50:  17%|█▋        | 81/475 [00:02<00:11, 33.45it/s]
Epoch 26/50:  18%|█▊        | 85/475 [00:02<00:11, 33.39it/s]
Epoch 26/50:  19%|█▊        | 89/475 [00:02<00:11, 34.94it/s]
Epoch 26/50:  20%|█▉        | 93/475 [00:02<00:10, 35.29it/s]
Epoch 26/50:  20%|██        | 97/475 [00:03<00:10, 35.92it/s]
Epoch 26/50:  21%|██▏       | 101/475 [00:03<00:10, 35.47it/s]
Epoch 26/50:  22%|██▏       | 105/475 [00:03<00:10, 35.97it/s]
Epoch 26/50:  23%|██▎       | 109/475 [00:03<00:10, 34.67it/s]
Epoch 26/50:  24%|██▍       | 113/475 [00:03<00:10, 34.54it/s]
Epoch 26/50:  25%|██▍       | 117/475 [00:03<00:11, 32.15it/s]
Epoch 26/50:  25%|██▌       | 121/475 [00:03<00:11, 29.77it/s]
Epoch 26/50:  26%|██▋       | 125/475 [00:03<00:11, 29.81it/s]
Epoch 26/50:  27%|██▋       | 129/475 [00:04<00:12, 27.96it/s]
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Epoch 26/50:  28%|██▊       | 135/475 [00:04<00:15, 21.92it/s]
Epoch 26/50:  29%|██▉       | 139/475 [00:04<00:14, 23.96it/s]
Epoch 26/50:  30%|██▉       | 142/475 [00:04<00:13, 23.91it/s]
Epoch 26/50:  31%|███       | 145/475 [00:04<00:13, 24.77it/s]
Epoch 26/50:  31%|███       | 148/475 [00:04<00:13, 24.08it/s]
Epoch 26/50:  32%|███▏      | 151/475 [00:05<00:13, 23.21it/s]
Epoch 26/50:  32%|███▏      | 154/475 [00:05<00:15, 20.91it/s]
Epoch 26/50:  33%|███▎      | 157/475 [00:05<00:15, 19.93it/s]
Epoch 26/50:  34%|███▎      | 160/475 [00:05<00:14, 21.58it/s]
Epoch 26/50:  34%|███▍      | 163/475 [00:05<00:13, 22.80it/s]
Epoch 26/50:  35%|███▍      | 166/475 [00:05<00:13, 23.11it/s]
Epoch 26/50:  36%|███▌      | 170/475 [00:05<00:12, 25.30it/s]
Epoch 26/50:  36%|███▋      | 173/475 [00:05<00:11, 25.19it/s]
Epoch 26/50:  37%|███▋      | 176/475 [00:06<00:13, 22.90it/s]
Epoch 26/50:  38%|███▊      | 179/475 [00:06<00:15, 18.63it/s]
Epoch 26/50:  38%|███▊      | 182/475 [00:06<00:16, 17.71it/s]
Epoch 26/50:  39%|███▉      | 185/475 [00:06<00:15, 19.14it/s]
Epoch 26/50:  40%|███▉      | 189/475 [00:06<00:12, 22.40it/s]
Epoch 26/50:  40%|████      | 192/475 [00:06<00:11, 23.86it/s]
Epoch 26/50:  41%|████▏     | 196/475 [00:07<00:10, 27.35it/s]
Epoch 26/50:  42%|████▏     | 200/475 [00:07<00:09, 29.42it/s]
Epoch 26/50:  43%|████▎     | 204/475 [00:07<00:08, 30.81it/s]
Epoch 26/50:  44%|████▍     | 208/475 [00:07<00:08, 31.07it/s]
Epoch 26/50:  45%|████▍     | 212/475 [00:07<00:08, 30.71it/s]
Epoch 26/50:  45%|████▌     | 216/475 [00:07<00:08, 29.89it/s]
Epoch 26/50:  46%|████▋     | 220/475 [00:07<00:08, 29.05it/s]
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Epoch 26/50:  48%|████▊     | 228/475 [00:08<00:07, 31.74it/s]
Epoch 26/50:  49%|████▉     | 232/475 [00:08<00:07, 32.08it/s]
Epoch 26/50:  50%|████▉     | 236/475 [00:08<00:07, 33.22it/s]
Epoch 26/50:  51%|█████     | 240/475 [00:08<00:06, 34.01it/s]
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Epoch 26/50:  54%|█████▍    | 257/475 [00:09<00:08, 24.63it/s]
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Epoch 26/50:  56%|█████▌    | 266/475 [00:09<00:08, 25.36it/s]
Epoch 26/50:  57%|█████▋    | 270/475 [00:09<00:07, 27.24it/s]
Epoch 26/50:  57%|█████▋    | 273/475 [00:09<00:07, 25.74it/s]
Epoch 26/50:  58%|█████▊    | 276/475 [00:09<00:07, 25.12it/s]
Epoch 26/50:  59%|█████▊    | 279/475 [00:10<00:08, 23.86it/s]
Epoch 26/50:  59%|█████▉    | 282/475 [00:10<00:08, 23.95it/s]
Epoch 26/50:  60%|██████    | 285/475 [00:10<00:09, 20.26it/s]
Epoch 26/50:  61%|██████    | 288/475 [00:10<00:08, 21.34it/s]
Epoch 26/50:  61%|██████▏   | 291/475 [00:10<00:07, 23.33it/s]
Epoch 26/50:  62%|██████▏   | 295/475 [00:10<00:06, 25.91it/s]
Epoch 26/50:  63%|██████▎   | 298/475 [00:10<00:07, 25.10it/s]
Epoch 26/50:  63%|██████▎   | 301/475 [00:10<00:07, 24.49it/s]
Epoch 26/50:  64%|██████▍   | 304/475 [00:11<00:07, 22.87it/s]
Epoch 26/50:  65%|██████▍   | 307/475 [00:11<00:07, 21.88it/s]
Epoch 26/50:  65%|██████▌   | 311/475 [00:11<00:06, 25.52it/s]
Epoch 26/50:  67%|██████▋   | 316/475 [00:11<00:05, 29.95it/s]
Epoch 26/50:  68%|██████▊   | 321/475 [00:11<00:04, 33.75it/s]
Epoch 26/50:  68%|██████▊   | 325/475 [00:11<00:04, 33.21it/s]
Epoch 26/50:  69%|██████▉   | 329/475 [00:11<00:04, 34.29it/s]
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Epoch 26/50:  74%|███████▎  | 350/475 [00:12<00:03, 34.09it/s]
Epoch 26/50:  75%|███████▍  | 355/475 [00:12<00:03, 37.68it/s]
Epoch 26/50:  76%|███████▌  | 359/475 [00:12<00:03, 38.23it/s]
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Epoch 26/50:  78%|███████▊  | 372/475 [00:13<00:02, 38.04it/s]
Epoch 26/50:  79%|███████▉  | 377/475 [00:13<00:02, 39.14it/s]
Epoch 26/50:  80%|████████  | 382/475 [00:13<00:02, 40.18it/s]
Epoch 26/50:  81%|████████▏ | 387/475 [00:13<00:02, 40.59it/s]
Epoch 26/50:  83%|████████▎ | 392/475 [00:13<00:01, 41.51it/s]
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Epoch 26/50:  89%|████████▉ | 423/475 [00:14<00:01, 40.74it/s]
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Epoch 26/50:  96%|█████████▋| 458/475 [00:14<00:00, 44.78it/s]
Epoch 26/50:  97%|█████████▋| 463/475 [00:15<00:00, 44.24it/s]
Epoch 26/50:  99%|█████████▊| 468/475 [00:15<00:00, 43.10it/s]
Epoch 26/50: 100%|█████████▉| 473/475 [00:15<00:00, 43.05it/s]
Epoch 26/50: 100%|██████████| 475/475 [00:15<00:00, 30.66it/s]
val_loss: 0.27163854680563276
val_mIoU: 0.5851351552579641
val_accuracy: 0.7561466578433388

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Epoch 27/50: 100%|██████████| 27/27 [00:10<00:00,  2.66it/s]
train_loss: 0.10813761171367434
train_mIoU: 0.8246668451515717
train_accuracy: 0.9144228504189842

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Epoch 27/50:  53%|█████▎    | 253/475 [00:08<00:05, 41.97it/s]
Epoch 27/50:  54%|█████▍    | 258/475 [00:08<00:04, 43.65it/s]
Epoch 27/50:  55%|█████▌    | 263/475 [00:09<00:05, 40.96it/s]
Epoch 27/50:  56%|█████▋    | 268/475 [00:09<00:04, 42.86it/s]
Epoch 27/50:  57%|█████▋    | 273/475 [00:09<00:05, 39.72it/s]
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Epoch 27/50:  61%|██████    | 289/475 [00:09<00:04, 41.94it/s]
Epoch 27/50:  62%|██████▏   | 295/475 [00:09<00:03, 45.15it/s]
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Epoch 27/50:  68%|██████▊   | 321/475 [00:10<00:03, 45.63it/s]
Epoch 27/50:  69%|██████▊   | 326/475 [00:10<00:03, 42.94it/s]
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Epoch 27/50:  71%|███████   | 337/475 [00:10<00:03, 39.37it/s]
Epoch 27/50:  72%|███████▏  | 342/475 [00:10<00:03, 40.11it/s]
Epoch 27/50:  73%|███████▎  | 348/475 [00:10<00:02, 42.81it/s]
Epoch 27/50:  74%|███████▍  | 353/475 [00:11<00:02, 43.10it/s]
Epoch 27/50:  75%|███████▌  | 358/475 [00:11<00:02, 39.35it/s]
Epoch 27/50:  76%|███████▋  | 363/475 [00:11<00:02, 39.89it/s]
Epoch 27/50:  77%|███████▋  | 368/475 [00:11<00:02, 40.76it/s]
Epoch 27/50:  79%|███████▊  | 374/475 [00:11<00:02, 44.23it/s]
Epoch 27/50:  80%|████████  | 380/475 [00:11<00:02, 46.42it/s]
Epoch 27/50:  81%|████████  | 385/475 [00:11<00:02, 43.80it/s]
Epoch 27/50:  82%|████████▏ | 391/475 [00:11<00:01, 46.13it/s]
Epoch 27/50:  83%|████████▎ | 396/475 [00:12<00:01, 40.37it/s]
Epoch 27/50:  84%|████████▍ | 401/475 [00:12<00:01, 41.62it/s]
Epoch 27/50:  85%|████████▌ | 406/475 [00:12<00:01, 40.72it/s]
Epoch 27/50:  87%|████████▋ | 411/475 [00:12<00:01, 41.84it/s]
Epoch 27/50:  88%|████████▊ | 417/475 [00:12<00:01, 43.58it/s]
Epoch 27/50:  89%|████████▉ | 422/475 [00:12<00:01, 42.42it/s]
Epoch 27/50:  90%|████████▉ | 427/475 [00:12<00:01, 43.20it/s]
Epoch 27/50:  91%|█████████ | 432/475 [00:12<00:01, 41.25it/s]
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Epoch 27/50:  93%|█████████▎| 442/475 [00:13<00:00, 44.03it/s]
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Epoch 27/50:  95%|█████████▌| 452/475 [00:13<00:00, 44.44it/s]
Epoch 27/50:  96%|█████████▌| 457/475 [00:13<00:00, 44.34it/s]
Epoch 27/50:  97%|█████████▋| 462/475 [00:13<00:00, 44.98it/s]
Epoch 27/50:  98%|█████████▊| 467/475 [00:13<00:00, 44.49it/s]
Epoch 27/50:  99%|█████████▉| 472/475 [00:13<00:00, 45.88it/s]
Epoch 27/50: 100%|██████████| 475/475 [00:13<00:00, 33.94it/s]
val_loss: 0.2584309164473885
val_mIoU: 0.55958263570525
val_accuracy: 0.7224045281661184

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Epoch 28/50: 100%|██████████| 27/27 [00:09<00:00,  2.74it/s]
train_loss: 0.106670498572014
train_mIoU: 0.826326290748233
train_accuracy: 0.9152845967848909

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Epoch 28/50: 100%|██████████| 475/475 [00:11<00:00, 40.84it/s]
val_loss: 0.2861536767608241
val_mIoU: 0.514050366774076
val_accuracy: 0.6806067215768914

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Epoch 29/50: 100%|██████████| 27/27 [00:10<00:00,  2.68it/s]
train_loss: 0.1073878346769898
train_mIoU: 0.8257398009537228
train_accuracy: 0.9149964665361534

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Epoch 29/50:  91%|█████████ | 432/475 [00:10<00:01, 40.77it/s]
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Epoch 29/50: 100%|██████████| 475/475 [00:11<00:00, 40.11it/s]
val_loss: 0.24087653185191907
val_mIoU: 0.5980506848721187
val_accuracy: 0.7561905067845395

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Epoch 30/50:  78%|███████▊  | 21/27 [00:07<00:02,  2.68it/s]
Epoch 30/50:  81%|████████▏ | 22/27 [00:08<00:01,  2.52it/s]
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Epoch 30/50: 100%|██████████| 27/27 [00:09<00:00,  2.78it/s]
train_loss: 0.10627884307393322
train_mIoU: 0.8272961465864943
train_accuracy: 0.9158486002222995

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Epoch 30/50:  80%|████████  | 382/475 [00:09<00:02, 43.45it/s]
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Epoch 30/50:  84%|████████▎ | 397/475 [00:10<00:01, 44.96it/s]
Epoch 30/50:  85%|████████▍ | 402/475 [00:10<00:01, 44.20it/s]
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Epoch 30/50:  87%|████████▋ | 412/475 [00:10<00:01, 43.40it/s]
Epoch 30/50:  88%|████████▊ | 417/475 [00:10<00:01, 40.79it/s]
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Epoch 30/50:  90%|████████▉ | 426/475 [00:10<00:01, 39.49it/s]
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Epoch 30/50:  95%|█████████▍| 450/475 [00:11<00:00, 41.56it/s]
Epoch 30/50:  96%|█████████▌| 456/475 [00:11<00:00, 44.27it/s]
Epoch 30/50:  97%|█████████▋| 461/475 [00:11<00:00, 38.93it/s]
Epoch 30/50:  98%|█████████▊| 466/475 [00:11<00:00, 40.47it/s]
Epoch 30/50:  99%|█████████▉| 472/475 [00:11<00:00, 43.25it/s]
Epoch 30/50: 100%|██████████| 475/475 [00:12<00:00, 39.54it/s]
val_loss: 0.2495820877426549
val_mIoU: 0.5716856310392786
val_accuracy: 0.7326412160773026

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Epoch 31/50:   4%|▎         | 1/27 [00:00<00:11,  2.21it/s]
Epoch 31/50:   7%|▋         | 2/27 [00:00<00:10,  2.36it/s]
Epoch 31/50:  11%|█         | 3/27 [00:01<00:09,  2.59it/s]
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Epoch 31/50:  63%|██████▎   | 17/27 [00:05<00:02,  3.51it/s]
Epoch 31/50:  67%|██████▋   | 18/27 [00:06<00:02,  3.03it/s]
Epoch 31/50:  70%|███████   | 19/27 [00:06<00:02,  3.39it/s]
Epoch 31/50:  74%|███████▍  | 20/27 [00:06<00:02,  3.08it/s]
Epoch 31/50:  78%|███████▊  | 21/27 [00:07<00:01,  3.41it/s]
Epoch 31/50:  81%|████████▏ | 22/27 [00:07<00:01,  3.12it/s]
Epoch 31/50:  85%|████████▌ | 23/27 [00:07<00:01,  2.81it/s]
Epoch 31/50:  89%|████████▉ | 24/27 [00:08<00:00,  3.18it/s]
Epoch 31/50:  93%|█████████▎| 25/27 [00:08<00:00,  3.09it/s]
Epoch 31/50:  96%|█████████▋| 26/27 [00:08<00:00,  2.89it/s]
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Epoch 31/50: 100%|██████████| 27/27 [00:09<00:00,  2.95it/s]
train_loss: 0.10598701073063745
train_mIoU: 0.8280247058342323
train_accuracy: 0.9162510463169643

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Epoch 31/50: 100%|██████████| 475/475 [00:11<00:00, 41.22it/s]
val_loss: 0.25215563272175034
val_mIoU: 0.5734077013179681
val_accuracy: 0.7348021175986842

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Epoch 32/50: 100%|██████████| 27/27 [00:09<00:00,  2.84it/s]
train_loss: 0.10532666853180638
train_mIoU: 0.8287549018986904
train_accuracy: 0.9166255120092031

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Epoch 32/50:  85%|████████▍ | 402/475 [00:09<00:01, 39.89it/s]
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Epoch 32/50:  89%|████████▉ | 422/475 [00:10<00:01, 42.62it/s]
Epoch 32/50:  90%|█████████ | 428/475 [00:10<00:01, 46.50it/s]
Epoch 32/50:  91%|█████████ | 433/475 [00:10<00:01, 40.27it/s]
Epoch 32/50:  92%|█████████▏| 438/475 [00:10<00:00, 40.15it/s]
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Epoch 32/50:  97%|█████████▋| 459/475 [00:11<00:00, 40.23it/s]
Epoch 32/50:  98%|█████████▊| 464/475 [00:11<00:00, 41.76it/s]
Epoch 32/50:  99%|█████████▉| 470/475 [00:11<00:00, 44.47it/s]
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Epoch 32/50: 100%|██████████| 475/475 [00:11<00:00, 41.47it/s]
val_loss: 0.3364322725095247
val_mIoU: 0.4534851153260141
val_accuracy: 0.6240218634354441

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Epoch 33/50: 100%|██████████| 27/27 [00:09<00:00,  2.84it/s]
train_loss: 0.1049572538446497
train_mIoU: 0.8293170963973299
train_accuracy: 0.916929294130562

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Epoch 33/50: 100%|██████████| 475/475 [00:11<00:00, 40.23it/s]
val_loss: 0.25135971759494985
val_mIoU: 0.5894671054110981
val_accuracy: 0.7499318976151316

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Epoch 34/50: 100%|██████████| 27/27 [00:09<00:00,  2.91it/s]
train_loss: 0.10458178873415347
train_mIoU: 0.8298635330434833
train_accuracy: 0.9172133148693647

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Epoch 34/50:  77%|███████▋  | 365/475 [00:08<00:02, 45.61it/s]
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Epoch 34/50:  85%|████████▍ | 402/475 [00:09<00:01, 41.50it/s]
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Epoch 34/50:  87%|████████▋ | 412/475 [00:09<00:01, 37.92it/s]
Epoch 34/50:  88%|████████▊ | 418/475 [00:09<00:01, 41.36it/s]
Epoch 34/50:  89%|████████▉ | 423/475 [00:10<00:01, 40.51it/s]
Epoch 34/50:  90%|█████████ | 428/475 [00:10<00:01, 39.78it/s]
Epoch 34/50:  91%|█████████ | 433/475 [00:10<00:01, 38.95it/s]
Epoch 34/50:  92%|█████████▏| 438/475 [00:10<00:00, 40.54it/s]
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Epoch 34/50:  95%|█████████▌| 452/475 [00:10<00:00, 35.26it/s]
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Epoch 34/50:  97%|█████████▋| 461/475 [00:11<00:00, 38.02it/s]
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Epoch 34/50: 100%|██████████| 475/475 [00:11<00:00, 41.55it/s]
val_loss: 0.23301059773093777
val_mIoU: 0.6168144463949433
val_accuracy: 0.7735687577097039

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Epoch 35/50: 100%|██████████| 27/27 [00:09<00:00,  2.93it/s]
train_loss: 0.10389315419726902
train_mIoU: 0.8310924947136596
train_accuracy: 0.9178935995704954

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Epoch 35/50: 100%|██████████| 475/475 [00:11<00:00, 42.73it/s]
val_loss: 0.2692179096372504
val_mIoU: 0.5398619207167655
val_accuracy: 0.7036704615542763

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Epoch 36/50:  67%|██████▋   | 18/27 [00:06<00:03,  2.51it/s]
Epoch 36/50:  70%|███████   | 19/27 [00:06<00:03,  2.62it/s]
Epoch 36/50:  74%|███████▍  | 20/27 [00:07<00:02,  2.97it/s]
Epoch 36/50:  78%|███████▊  | 21/27 [00:07<00:02,  2.73it/s]
Epoch 36/50:  81%|████████▏ | 22/27 [00:07<00:01,  2.77it/s]
Epoch 36/50:  85%|████████▌ | 23/27 [00:08<00:01,  3.09it/s]
Epoch 36/50:  89%|████████▉ | 24/27 [00:08<00:01,  2.90it/s]
Epoch 36/50:  93%|█████████▎| 25/27 [00:08<00:00,  2.92it/s]
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Epoch 36/50: 100%|██████████| 27/27 [00:09<00:00,  2.84it/s]
train_loss: 0.10305673711829716
train_mIoU: 0.8319668179620128
train_accuracy: 0.9183109828404018

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Epoch 36/50: 100%|██████████| 475/475 [00:11<00:00, 42.91it/s]
val_loss: 0.30440720821681777
val_mIoU: 0.5315419650216118
val_accuracy: 0.704795564350329

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Epoch 37/50: 100%|██████████| 27/27 [00:09<00:00,  2.83it/s]
train_loss: 0.10327590532876828
train_mIoU: 0.8320762784673983
train_accuracy: 0.9184201171303242

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Epoch 37/50:  87%|████████▋ | 414/475 [00:09<00:01, 42.34it/s]
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Epoch 37/50:  89%|████████▉ | 424/475 [00:10<00:01, 42.67it/s]
Epoch 37/50:  90%|█████████ | 429/475 [00:10<00:01, 40.64it/s]
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Epoch 37/50:  96%|█████████▌| 454/475 [00:10<00:00, 42.12it/s]
Epoch 37/50:  97%|█████████▋| 460/475 [00:10<00:00, 43.63it/s]
Epoch 37/50:  98%|█████████▊| 465/475 [00:11<00:00, 42.07it/s]
Epoch 37/50:  99%|█████████▉| 470/475 [00:11<00:00, 41.77it/s]
Epoch 37/50: 100%|██████████| 475/475 [00:11<00:00, 41.96it/s]
val_loss: 0.26505693259992097
val_mIoU: 0.5447333810909801
val_accuracy: 0.7085460783305921

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Epoch 38/50:  78%|███████▊  | 21/27 [00:07<00:01,  3.11it/s]
Epoch 38/50:  81%|████████▏ | 22/27 [00:07<00:01,  2.70it/s]
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Epoch 38/50: 100%|██████████| 27/27 [00:09<00:00,  2.89it/s]
train_loss: 0.10399674982936294
train_mIoU: 0.831003539319614
train_accuracy: 0.9178437494282421

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val_loss: 0.28312193814076875
val_mIoU: 0.5488835201196723
val_accuracy: 0.7116130949321546

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train_loss: 0.10342067369708309
train_mIoU: 0.8315466895803827
train_accuracy: 0.9180952157013869

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val_loss: 0.28869709146650213
val_mIoU: 0.5141346682917629
val_accuracy: 0.6807684968647204

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train_loss: 0.10095507541188488
train_mIoU: 0.8355253884082219
train_accuracy: 0.920256281904091

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Epoch 40/50: 100%|██████████| 475/475 [00:11<00:00, 39.69it/s]
val_loss: 0.27156055839438187
val_mIoU: 0.559248519066881
val_accuracy: 0.7245922530324835

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train_loss: 0.10001607018488425
train_mIoU: 0.8368015546022233
train_accuracy: 0.9209120882199575

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val_loss: 0.33771035357525475
val_mIoU: 0.47640789326723887
val_accuracy: 0.6472362638774671

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train_loss: 0.10349530975023906
train_mIoU: 0.8316988027851768
train_accuracy: 0.9182337240536264

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Epoch 42/50: 100%|██████████| 475/475 [00:10<00:00, 43.86it/s]
val_loss: 0.3438119545735811
val_mIoU: 0.5395162947205862
val_accuracy: 0.736076371042352

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Epoch 43/50: 100%|██████████| 27/27 [00:09<00:00,  2.95it/s]
train_loss: 0.10275980995761023
train_mIoU: 0.8325766906782867
train_accuracy: 0.9186611488217213

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Epoch 43/50: 100%|██████████| 475/475 [00:11<00:00, 41.47it/s]
val_loss: 0.27347054017217537
val_mIoU: 0.5763375235142196
val_accuracy: 0.7426942845394737

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train_loss: 0.10246631999810536
train_mIoU: 0.8333803521378753
train_accuracy: 0.9191062623499707

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Epoch 44/50:  97%|█████████▋| 462/475 [00:10<00:00, 49.57it/s]
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Epoch 44/50: 100%|██████████| 475/475 [00:10<00:00, 43.20it/s]
val_loss: 0.29632651303943836
val_mIoU: 0.5640935500952733
val_accuracy: 0.7421116557874178

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train_loss: 0.09945283223081518
train_mIoU: 0.8376349254417508
train_accuracy: 0.9213479821520053

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val_loss: 0.3304888102882787
val_mIoU: 0.5738579139866256
val_accuracy: 0.7712440249794408

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train_loss: 0.10128486184058366
train_mIoU: 0.8347135547605005
train_accuracy: 0.9198021274539849

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val_loss: 0.2654873718086042
val_mIoU: 0.5442838509168877
val_accuracy: 0.7073825876336348

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train_loss: 0.09916298505332735
train_mIoU: 0.8382628578880181
train_accuracy: 0.9217006495741548

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Epoch 47/50: 100%|██████████| 475/475 [00:10<00:00, 44.29it/s]
val_loss: 0.2353630042703528
val_mIoU: 0.6005868408689818
val_accuracy: 0.7578413471422697

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Epoch 48/50:  81%|████████▏ | 22/27 [00:07<00:01,  3.15it/s]
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Epoch 48/50:  89%|████████▉ | 24/27 [00:08<00:00,  3.22it/s]
Epoch 48/50:  93%|█████████▎| 25/27 [00:08<00:00,  3.17it/s]
Epoch 48/50:  96%|█████████▋| 26/27 [00:09<00:00,  2.85it/s]
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Epoch 48/50: 100%|██████████| 27/27 [00:09<00:00,  2.90it/s]
train_loss: 0.09990600165393618
train_mIoU: 0.8371030146898712
train_accuracy: 0.9210867245247548

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Epoch 48/50:  85%|████████▌ | 405/475 [00:09<00:01, 50.51it/s]
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Epoch 48/50:  90%|████████▉ | 426/475 [00:09<00:01, 44.56it/s]
Epoch 48/50:  91%|█████████ | 431/475 [00:09<00:01, 42.42it/s]
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Epoch 48/50:  95%|█████████▌| 452/475 [00:10<00:00, 42.84it/s]
Epoch 48/50:  96%|█████████▌| 457/475 [00:10<00:00, 43.18it/s]
Epoch 48/50:  97%|█████████▋| 463/475 [00:10<00:00, 46.87it/s]
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Epoch 48/50: 100%|██████████| 475/475 [00:11<00:00, 43.08it/s]
val_loss: 0.2578895716290725
val_mIoU: 0.5620104256288163
val_accuracy: 0.7245297080592106

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train_loss: 0.09883598514177182
train_mIoU: 0.8382633361906209
train_accuracy: 0.921661448422863

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val_loss: 0.3433395166773545
val_mIoU: 0.45449772271881567
val_accuracy: 0.6250581440172698
loading model for testing
Testing...

  0%|          | 0/14 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/home/blue/projects/MoNuSeg/main.py", line 560, in <module>
    main()
  File "/home/blue/projects/MoNuSeg/main.py", line 480, in main
    rslt_color = result_recolor(rslt.cpu().detach().numpy())
  File "/home/blue/projects/MoNuSeg/auxilary/utils.py", line 30, in result_recolor
    colormap = [config["class1"],config["class2"]] # Purple, Orange, Yellow, Black
KeyError: 'class3'

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