log-11-23.txt

wandb: Currently logged in as: bluesaiyancodes. Use `wandb login --relogin` to force relogin
wandb: wandb version 0.16.0 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.15.8
wandb: Run data is saved locally in /home/blue/projects/MoNuSeg/wandb/run-20231123_162105-hm9cm3p2
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run zesty-wave-99
wandb: ⭐️ View project at https://wandb.ai/bluesaiyancodes/monunet-segmenation
wandb: 🚀 View run at https://wandb.ai/bluesaiyancodes/monunet-segmenation/runs/hm9cm3p2
PreRun: Creating required directories
Directory Outputs/ already exists
Directory log/ already exists
PreRun: generating experiment config file
Using cuda device
configuring data loaders
loading Images from path: Dataset/trainNormal/

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Using cache found in /home/blue/.cache/torch/hub/facebookresearch_dinov2_main
Loading Features
Applying DBSCAN
Unique clusters: [-1  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38]
Directory Outputs/Plots/ already exists
Sampling Initialization Complete
configuring model - UNET 3+
saving model summary
logging parameters
starting training

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Epoch 0/1:  93%|█████████▎| 157/168 [03:18<00:11,  1.00s/it]
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Epoch 0/1:  96%|█████████▋| 162/168 [03:22<00:05,  1.00it/s]
Epoch 0/1:  97%|█████████▋| 163/168 [03:23<00:04,  1.01it/s]
Epoch 0/1:  98%|█████████▊| 164/168 [03:24<00:03,  1.02it/s]
Epoch 0/1:  98%|█████████▊| 165/168 [03:25<00:02,  1.02it/s]
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Epoch 0/1: : 1927it [33:12,  1.07s/it]
Epoch 0/1: : 1928it [33:13,  1.08s/it]
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Epoch 0/1: : 1932it [33:17,  1.03s/it]
Epoch 0/1: : 1933it [33:19,  1.11s/it]
Epoch 0/1: : 1934it [33:20,  1.11s/it]
Epoch 0/1: : 1935it [33:21,  1.08s/it]
Epoch 0/1: : 1936it [33:22,  1.06s/it]
Epoch 0/1: : 1937it [33:23,  1.03s/it]
Epoch 0/1: : 1938it [33:24,  1.01it/s]
Epoch 0/1: : 1939it [33:25,  1.02it/s]
Epoch 0/1: : 1940it [33:26,  1.04it/s]
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Epoch 0/1: : 1942it [33:28,  1.02it/s]
Epoch 0/1: : 1943it [33:29,  1.01it/s]
Epoch 0/1: : 1944it [33:30,  1.02s/it]
Epoch 0/1: : 1945it [33:31,  1.01it/s]
Epoch 0/1: : 1946it [33:32,  1.02s/it]
Epoch 0/1: : 1947it [33:33,  1.01it/s]
Epoch 0/1: : 1948it [33:34,  1.02it/s]
Epoch 0/1: : 1949it [33:35,  1.00it/s]
Epoch 0/1: : 1950it [33:36,  1.07s/it]
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Epoch 0/1: : 1952it [33:38,  1.01s/it]
Epoch 0/1: : 1953it [33:39,  1.01s/it]
Epoch 0/1: : 1954it [33:40,  1.01s/it]
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Epoch 0/1: : 1980it [34:06,  1.02s/it]
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Epoch 0/1: : 1984it [34:10,  1.04it/s]
Epoch 0/1: : 1985it [34:11,  1.04it/s]
Epoch 0/1: : 1986it [34:12,  1.02it/s]
Epoch 0/1: : 1987it [34:13,  1.02it/s]
Epoch 0/1: : 1988it [34:14,  1.03it/s]
Epoch 0/1: : 1989it [34:15,  1.02it/s]
Epoch 0/1: : 1990it [34:16,  1.02it/s]
Epoch 0/1: : 1991it [34:17,  1.02s/it]
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Epoch 0/1: : 1993it [34:19,  1.01s/it]
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Epoch 0/1: : 1995it [34:21,  1.01it/s]
Epoch 0/1: : 1996it [34:22,  1.01s/it]
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Epoch 0/1: : 2001it [34:27,  1.00s/it]
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Epoch 0/1: : 2013it [34:39,  1.02it/s]
Epoch 0/1: : 2014it [34:40,  1.02it/s]
Epoch 0/1: : 2015it [34:41,  1.03it/s]
Epoch 0/1: : 2016it [34:42,  1.01it/s]
Epoch 0/1: : 2017it [34:43,  1.01it/s]
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Epoch 0/1: : 2019it [34:45,  1.00s/it]
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Epoch 0/1: : 2021it [34:47,  1.03s/it]
Epoch 0/1: : 2022it [34:48,  1.02s/it]
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mode: train
train_loss: 5.729326385827291
train_mIoU: 0.5891953956723253
train_accuracy: 0.7895819617263218

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val_loss: 0.3169407083994712
val_mIoU: 0.6782558831126984
val_accuracy: 0.8382246711037376
saving model
loading model for testing
Testing...

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Saving Training Stats


 ================ confusion matrix ================ 


[[1.96847281e+08 2.27017670e+07]
 [2.76794500e+07 6.41985740e+07]]


 ================     val mIoUs    ================ 


0.6782558831126984


 ================    val accuracy   ================ 


0.8382246711037376


 ================    Dice Score   ================ 


0.8023699551737783


 ================       end        ================ 


elapsed time :  0.9  hours
Logging run time
generting loss and accuracy plot
wandb: Waiting for W&B process to finish... (success).
wandb: - 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: \ 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: | 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: / 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
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wandb: | 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: 
wandb: Run history:
wandb: train_accuracy ▁
wandb:     train_loss ▁
wandb:     train_mIoU ▁
wandb:   val_accuracy ▁
wandb:       val_loss ▁
wandb:       val_mIoU ▁
wandb: 
wandb: Run summary:
wandb: train_accuracy 0.78958
wandb:     train_loss 5.72933
wandb:     train_mIoU 0.5892
wandb:   val_accuracy 0.83822
wandb:       val_loss 0.31694
wandb:       val_mIoU 0.67826
wandb: 
wandb: 🚀 View run zesty-wave-99 at: https://wandb.ai/bluesaiyancodes/monunet-segmenation/runs/hm9cm3p2
wandb: ️⚡ View job at https://wandb.ai/bluesaiyancodes/monunet-segmenation/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjg5OTQ0NzM0/version_details/v24
wandb: Synced 5 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20231123_162105-hm9cm3p2/logs