BaseDataset: base_size 608, crop_size 576
BaseDataset: base_size 608, crop_size 576
EncNet(
  (pretrained): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (avgpool): AvgPool2d(kernel_size=7, stride=7, padding=0)
    (fc): Linear(in_features=2048, out_features=1000, bias=True)
  )
  (head): EncHead(
    (conv5): Sequential(
      (0): Conv2d(2048, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (encmodule): EncModule(
      (encoding): Sequential(
        (0): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace)
        (3): Encoding(N x 512=>32x512)
        (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): ReLU(inplace)
        (6): Mean()
      )
      (fc): Sequential(
        (0): Linear(in_features=512, out_features=512, bias=True)
        (1): Sigmoid()
      )
      (selayer): Linear(in_features=512, out_features=150, bias=True)
    )
    (conv6): Sequential(
      (0): Dropout2d(p=0.1)
      (1): Conv2d(512, 150, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  (auxlayer): FCNHead(
    (conv5): Sequential(
      (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
      (3): Dropout2d(p=0.1)
      (4): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1))
    )
  )
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Using poly LR Scheduler!
Starting Epoch: 0
Total Epoches: 160
Train loss: 3.261: 100%|██████████| 1263/1263 [18:53<00:00,  1.11it/s]
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=>Epoches 0, learning rate = 0.0100,                 previous best = 0.0000
pixAcc: 0.592, mIoU: 0.102: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 2.743: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
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=>Epoches 1, learning rate = 0.0099,                 previous best = 0.3467
pixAcc: 0.652, mIoU: 0.153: 100%|██████████| 125/125 [01:48<00:00,  1.15it/s]
Train loss: 2.558: 100%|██████████| 1263/1263 [18:55<00:00,  1.11it/s]
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=>Epoches 2, learning rate = 0.0099,                 previous best = 0.4028
pixAcc: 0.652, mIoU: 0.170: 100%|██████████| 125/125 [01:48<00:00,  1.15it/s]
Train loss: 2.452: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
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=>Epoches 3, learning rate = 0.0098,                 previous best = 0.4109
pixAcc: 0.666, mIoU: 0.188: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 2.383: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
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=>Epoches 4, learning rate = 0.0098,                 previous best = 0.4270
pixAcc: 0.648, mIoU: 0.160: 100%|██████████| 125/125 [01:48<00:00,  1.15it/s]
Train loss: 2.316: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 5, learning rate = 0.0097,                 previous best = 0.4270
pixAcc: 0.682, mIoU: 0.205: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 2.255: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
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=>Epoches 6, learning rate = 0.0097,                 previous best = 0.4436
pixAcc: 0.689, mIoU: 0.215: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 2.228: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
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=>Epoches 7, learning rate = 0.0096,                 previous best = 0.4521
pixAcc: 0.692, mIoU: 0.219: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 2.187: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
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=>Epoches 8, learning rate = 0.0095,                 previous best = 0.4555
pixAcc: 0.677, mIoU: 0.230: 100%|██████████| 125/125 [01:49<00:00,  1.15it/s]
Train loss: 2.141: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 9, learning rate = 0.0095,                 previous best = 0.4555
pixAcc: 0.704, mIoU: 0.246: 100%|██████████| 125/125 [01:50<00:00,  1.14it/s]
Train loss: 2.127: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
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=>Epoches 10, learning rate = 0.0094,                 previous best = 0.4753
pixAcc: 0.701, mIoU: 0.248: 100%|██████████| 125/125 [01:51<00:00,  1.13it/s]
Train loss: 2.080: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
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=>Epoches 11, learning rate = 0.0094,                 previous best = 0.4753
pixAcc: 0.702, mIoU: 0.244: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 2.077: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
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=>Epoches 12, learning rate = 0.0093,                 previous best = 0.4753
pixAcc: 0.694, mIoU: 0.251: 100%|██████████| 125/125 [01:49<00:00,  1.15it/s]
Train loss: 2.023: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
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=>Epoches 13, learning rate = 0.0093,                 previous best = 0.4753
pixAcc: 0.720, mIoU: 0.263: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 2.022: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
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=>Epoches 14, learning rate = 0.0092,                 previous best = 0.4912
pixAcc: 0.703, mIoU: 0.250: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 2.002: 100%|██████████| 1263/1263 [18:43<00:00,  1.12it/s]
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=>Epoches 15, learning rate = 0.0092,                 previous best = 0.4912
pixAcc: 0.717, mIoU: 0.266: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.963: 100%|██████████| 1263/1263 [18:42<00:00,  1.12it/s]
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=>Epoches 16, learning rate = 0.0091,                 previous best = 0.4915
pixAcc: 0.697, mIoU: 0.251: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.957: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
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=>Epoches 17, learning rate = 0.0090,                 previous best = 0.4915
pixAcc: 0.715, mIoU: 0.268: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.957: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
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=>Epoches 18, learning rate = 0.0090,                 previous best = 0.4919
pixAcc: 0.718, mIoU: 0.278: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.928: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
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=>Epoches 19, learning rate = 0.0089,                 previous best = 0.4979
pixAcc: 0.721, mIoU: 0.284: 100%|██████████| 125/125 [01:51<00:00,  1.13it/s]
Train loss: 1.925: 100%|██████████| 1263/1263 [18:43<00:00,  1.12it/s]
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=>Epoches 20, learning rate = 0.0089,                 previous best = 0.5029
pixAcc: 0.714, mIoU: 0.269: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.905: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 21, learning rate = 0.0088,                 previous best = 0.5029
pixAcc: 0.702, mIoU: 0.279: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.891: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 22, learning rate = 0.0088,                 previous best = 0.5029
pixAcc: 0.717, mIoU: 0.287: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.876: 100%|██████████| 1263/1263 [18:43<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 23, learning rate = 0.0087,                 previous best = 0.5029
pixAcc: 0.724, mIoU: 0.290: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.859: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 24, learning rate = 0.0086,                 previous best = 0.5074
pixAcc: 0.713, mIoU: 0.276: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.851: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 25, learning rate = 0.0086,                 previous best = 0.5074
pixAcc: 0.710, mIoU: 0.276: 100%|██████████| 125/125 [01:50<00:00,  1.14it/s]
Train loss: 1.843: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 26, learning rate = 0.0085,                 previous best = 0.5074
pixAcc: 0.715, mIoU: 0.284: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.823: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 27, learning rate = 0.0085,                 previous best = 0.5074
pixAcc: 0.715, mIoU: 0.282: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.820: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 28, learning rate = 0.0084,                 previous best = 0.5074
pixAcc: 0.695, mIoU: 0.274: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.802: 100%|██████████| 1263/1263 [18:42<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 29, learning rate = 0.0084,                 previous best = 0.5074
pixAcc: 0.723, mIoU: 0.292: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.797: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 30, learning rate = 0.0083,                 previous best = 0.5077
pixAcc: 0.723, mIoU: 0.288: 100%|██████████| 125/125 [01:50<00:00,  1.14it/s]
Train loss: 1.787: 100%|██████████| 1263/1263 [18:35<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 31, learning rate = 0.0082,                 previous best = 0.5077
pixAcc: 0.732, mIoU: 0.302: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.783: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 32, learning rate = 0.0082,                 previous best = 0.5171
pixAcc: 0.721, mIoU: 0.289: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.772: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 33, learning rate = 0.0081,                 previous best = 0.5171
pixAcc: 0.738, mIoU: 0.314: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.762: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 34, learning rate = 0.0081,                 previous best = 0.5260
pixAcc: 0.698, mIoU: 0.269: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.753: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 35, learning rate = 0.0080,                 previous best = 0.5260
pixAcc: 0.725, mIoU: 0.293: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.735: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 36, learning rate = 0.0080,                 previous best = 0.5260
pixAcc: 0.723, mIoU: 0.302: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.721: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 37, learning rate = 0.0079,                 previous best = 0.5260
pixAcc: 0.734, mIoU: 0.304: 100%|██████████| 125/125 [01:49<00:00,  1.14it/s]
Train loss: 1.714: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 38, learning rate = 0.0078,                 previous best = 0.5260
pixAcc: 0.729, mIoU: 0.318: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.701: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 39, learning rate = 0.0078,                 previous best = 0.5260
pixAcc: 0.732, mIoU: 0.313: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.704: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 40, learning rate = 0.0077,                 previous best = 0.5260
pixAcc: 0.735, mIoU: 0.307: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.701: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 41, learning rate = 0.0077,                 previous best = 0.5260
pixAcc: 0.730, mIoU: 0.309: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.679: 100%|██████████| 1263/1263 [18:34<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 42, learning rate = 0.0076,                 previous best = 0.5260
pixAcc: 0.735, mIoU: 0.311: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.670: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 43, learning rate = 0.0075,                 previous best = 0.5260
pixAcc: 0.726, mIoU: 0.317: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.658: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 44, learning rate = 0.0075,                 previous best = 0.5260
pixAcc: 0.731, mIoU: 0.317: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.657: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 45, learning rate = 0.0074,                 previous best = 0.5260
pixAcc: 0.720, mIoU: 0.303: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.633: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 46, learning rate = 0.0074,                 previous best = 0.5260
pixAcc: 0.733, mIoU: 0.312: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.645: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 47, learning rate = 0.0073,                 previous best = 0.5260
pixAcc: 0.737, mIoU: 0.309: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.635: 100%|██████████| 1263/1263 [18:53<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 48, learning rate = 0.0073,                 previous best = 0.5260
pixAcc: 0.722, mIoU: 0.310: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.612: 100%|██████████| 1263/1263 [18:51<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 49, learning rate = 0.0072,                 previous best = 0.5260
pixAcc: 0.729, mIoU: 0.313: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 1.613: 100%|██████████| 1263/1263 [18:56<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 50, learning rate = 0.0071,                 previous best = 0.5260
pixAcc: 0.727, mIoU: 0.303: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.603: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 51, learning rate = 0.0071,                 previous best = 0.5260
pixAcc: 0.722, mIoU: 0.322: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.597: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 52, learning rate = 0.0070,                 previous best = 0.5260
pixAcc: 0.722, mIoU: 0.311: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.595: 100%|██████████| 1263/1263 [18:52<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 53, learning rate = 0.0070,                 previous best = 0.5260
pixAcc: 0.728, mIoU: 0.295: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.568: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 54, learning rate = 0.0069,                 previous best = 0.5260
pixAcc: 0.732, mIoU: 0.316: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.570: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 55, learning rate = 0.0068,                 previous best = 0.5260
pixAcc: 0.738, mIoU: 0.333: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.563: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 56, learning rate = 0.0068,                 previous best = 0.5356
pixAcc: 0.733, mIoU: 0.332: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.562: 100%|██████████| 1263/1263 [18:52<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 57, learning rate = 0.0067,                 previous best = 0.5356
pixAcc: 0.745, mIoU: 0.327: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 1.537: 100%|██████████| 1263/1263 [18:55<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 58, learning rate = 0.0067,                 previous best = 0.5362
pixAcc: 0.743, mIoU: 0.329: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.533: 100%|██████████| 1263/1263 [18:53<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 59, learning rate = 0.0066,                 previous best = 0.5362
pixAcc: 0.745, mIoU: 0.332: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.551: 100%|██████████| 1263/1263 [18:37<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 60, learning rate = 0.0066,                 previous best = 0.5385
pixAcc: 0.738, mIoU: 0.316: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 1.539: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 61, learning rate = 0.0065,                 previous best = 0.5385
pixAcc: 0.739, mIoU: 0.329: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.544: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 62, learning rate = 0.0064,                 previous best = 0.5385
pixAcc: 0.736, mIoU: 0.334: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.511: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 63, learning rate = 0.0064,                 previous best = 0.5385
pixAcc: 0.734, mIoU: 0.336: 100%|██████████| 125/125 [01:50<00:00,  1.13it/s]
Train loss: 1.519: 100%|██████████| 1263/1263 [18:37<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 64, learning rate = 0.0063,                 previous best = 0.5385
pixAcc: 0.741, mIoU: 0.329: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.510: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 65, learning rate = 0.0063,                 previous best = 0.5385
pixAcc: 0.736, mIoU: 0.322: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.506: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 66, learning rate = 0.0062,                 previous best = 0.5385
pixAcc: 0.737, mIoU: 0.322: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.496: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 67, learning rate = 0.0061,                 previous best = 0.5385
pixAcc: 0.747, mIoU: 0.330: 100%|██████████| 125/125 [01:52<00:00,  1.12it/s]
Train loss: 1.470: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 68, learning rate = 0.0061,                 previous best = 0.5386
pixAcc: 0.746, mIoU: 0.334: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.481: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 69, learning rate = 0.0060,                 previous best = 0.5399
pixAcc: 0.742, mIoU: 0.336: 100%|██████████| 125/125 [01:52<00:00,  1.12it/s]
Train loss: 1.469: 100%|██████████| 1263/1263 [18:37<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 70, learning rate = 0.0060,                 previous best = 0.5399
pixAcc: 0.744, mIoU: 0.328: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.451: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 71, learning rate = 0.0059,                 previous best = 0.5399
pixAcc: 0.750, mIoU: 0.340: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.446: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 72, learning rate = 0.0058,                 previous best = 0.5450
pixAcc: 0.750, mIoU: 0.337: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.463: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 73, learning rate = 0.0058,                 previous best = 0.5450
pixAcc: 0.747, mIoU: 0.339: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.431: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 74, learning rate = 0.0057,                 previous best = 0.5450
pixAcc: 0.749, mIoU: 0.336: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.430: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 75, learning rate = 0.0057,                 previous best = 0.5450
pixAcc: 0.740, mIoU: 0.335: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.438: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 76, learning rate = 0.0056,                 previous best = 0.5450
pixAcc: 0.735, mIoU: 0.336: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.419: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 77, learning rate = 0.0055,                 previous best = 0.5450
pixAcc: 0.750, mIoU: 0.341: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 1.420: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 78, learning rate = 0.0055,                 previous best = 0.5457
pixAcc: 0.750, mIoU: 0.342: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.410: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 79, learning rate = 0.0054,                 previous best = 0.5459
pixAcc: 0.754, mIoU: 0.346: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.399: 100%|██████████| 1263/1263 [18:49<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 80, learning rate = 0.0054,                 previous best = 0.5497
pixAcc: 0.747, mIoU: 0.348: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.374: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 81, learning rate = 0.0053,                 previous best = 0.5497
pixAcc: 0.747, mIoU: 0.344: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.361: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 82, learning rate = 0.0052,                 previous best = 0.5497
pixAcc: 0.750, mIoU: 0.344: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.370: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 83, learning rate = 0.0052,                 previous best = 0.5497
pixAcc: 0.755, mIoU: 0.348: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.361: 100%|██████████| 1263/1263 [18:42<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 84, learning rate = 0.0051,                 previous best = 0.5514
pixAcc: 0.744, mIoU: 0.342: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.351: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 85, learning rate = 0.0051,                 previous best = 0.5514
pixAcc: 0.756, mIoU: 0.350: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.345: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 86, learning rate = 0.0050,                 previous best = 0.5527
pixAcc: 0.742, mIoU: 0.337: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.325: 100%|██████████| 1263/1263 [18:28<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 87, learning rate = 0.0049,                 previous best = 0.5527
pixAcc: 0.751, mIoU: 0.349: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.354: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 88, learning rate = 0.0049,                 previous best = 0.5527
pixAcc: 0.754, mIoU: 0.360: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.293: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 89, learning rate = 0.0048,                 previous best = 0.5570
pixAcc: 0.757, mIoU: 0.354: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.317: 100%|██████████| 1263/1263 [18:33<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 90, learning rate = 0.0048,                 previous best = 0.5570
pixAcc: 0.755, mIoU: 0.350: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.329: 100%|██████████| 1263/1263 [18:47<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 91, learning rate = 0.0047,                 previous best = 0.5570
pixAcc: 0.740, mIoU: 0.338: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.321: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 92, learning rate = 0.0046,                 previous best = 0.5570
pixAcc: 0.750, mIoU: 0.351: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.310: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 93, learning rate = 0.0046,                 previous best = 0.5570
pixAcc: 0.757, mIoU: 0.357: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.292: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 94, learning rate = 0.0045,                 previous best = 0.5570
pixAcc: 0.745, mIoU: 0.354: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.306: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 95, learning rate = 0.0044,                 previous best = 0.5570
pixAcc: 0.753, mIoU: 0.353: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.284: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 96, learning rate = 0.0044,                 previous best = 0.5570
pixAcc: 0.750, mIoU: 0.347: 100%|██████████| 125/125 [01:53<00:00,  1.11it/s]
Train loss: 1.271: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 97, learning rate = 0.0043,                 previous best = 0.5570
pixAcc: 0.749, mIoU: 0.356: 100%|██████████| 125/125 [01:53<00:00,  1.11it/s]
Train loss: 1.272: 100%|██████████| 1263/1263 [18:50<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 98, learning rate = 0.0043,                 previous best = 0.5570
pixAcc: 0.751, mIoU: 0.347: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.258: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 99, learning rate = 0.0042,                 previous best = 0.5570
pixAcc: 0.753, mIoU: 0.351: 100%|██████████| 125/125 [01:53<00:00,  1.11it/s]
Train loss: 1.246: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 100, learning rate = 0.0041,                 previous best = 0.5570
pixAcc: 0.750, mIoU: 0.344: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.244: 100%|██████████| 1263/1263 [18:40<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 101, learning rate = 0.0041,                 previous best = 0.5570
pixAcc: 0.750, mIoU: 0.364: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.260: 100%|██████████| 1263/1263 [18:56<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 102, learning rate = 0.0040,                 previous best = 0.5570
pixAcc: 0.755, mIoU: 0.360: 100%|██████████| 125/125 [01:55<00:00,  1.08it/s]
Train loss: 1.254: 100%|██████████| 1263/1263 [18:52<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 103, learning rate = 0.0039,                 previous best = 0.5571
pixAcc: 0.746, mIoU: 0.357: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.215: 100%|██████████| 1263/1263 [18:42<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 104, learning rate = 0.0039,                 previous best = 0.5571
pixAcc: 0.761, mIoU: 0.366: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.209: 100%|██████████| 1263/1263 [18:43<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 105, learning rate = 0.0038,                 previous best = 0.5632
pixAcc: 0.762, mIoU: 0.363: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.193: 100%|██████████| 1263/1263 [18:40<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 106, learning rate = 0.0038,                 previous best = 0.5632
pixAcc: 0.764, mIoU: 0.371: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.187: 100%|██████████| 1263/1263 [18:52<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 107, learning rate = 0.0037,                 previous best = 0.5676
pixAcc: 0.762, mIoU: 0.377: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.184: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 108, learning rate = 0.0036,                 previous best = 0.5694
pixAcc: 0.760, mIoU: 0.368: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.175: 100%|██████████| 1263/1263 [18:48<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 109, learning rate = 0.0036,                 previous best = 0.5694
pixAcc: 0.756, mIoU: 0.365: 100%|██████████| 125/125 [01:55<00:00,  1.08it/s]
Train loss: 1.215: 100%|██████████| 1263/1263 [18:52<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 110, learning rate = 0.0035,                 previous best = 0.5694
pixAcc: 0.752, mIoU: 0.355: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.204: 100%|██████████| 1263/1263 [18:55<00:00,  1.11it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 111, learning rate = 0.0034,                 previous best = 0.5694
pixAcc: 0.760, mIoU: 0.367: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.154: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 112, learning rate = 0.0034,                 previous best = 0.5694
pixAcc: 0.760, mIoU: 0.367: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.144: 100%|██████████| 1263/1263 [18:37<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 113, learning rate = 0.0033,                 previous best = 0.5694
pixAcc: 0.761, mIoU: 0.370: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.146: 100%|██████████| 1263/1263 [18:40<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 114, learning rate = 0.0033,                 previous best = 0.5694
pixAcc: 0.758, mIoU: 0.373: 100%|██████████| 125/125 [01:52<00:00,  1.12it/s]
Train loss: 1.126: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 115, learning rate = 0.0032,                 previous best = 0.5694
pixAcc: 0.763, mIoU: 0.380: 100%|██████████| 125/125 [01:55<00:00,  1.08it/s]
Train loss: 1.131: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 116, learning rate = 0.0031,                 previous best = 0.5718
pixAcc: 0.762, mIoU: 0.373: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.133: 100%|██████████| 1263/1263 [18:33<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 117, learning rate = 0.0031,                 previous best = 0.5718
pixAcc: 0.762, mIoU: 0.378: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.123: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 118, learning rate = 0.0030,                 previous best = 0.5718
pixAcc: 0.763, mIoU: 0.371: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.101: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 119, learning rate = 0.0029,                 previous best = 0.5718
pixAcc: 0.767, mIoU: 0.378: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.089: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 120, learning rate = 0.0029,                 previous best = 0.5726
pixAcc: 0.766, mIoU: 0.380: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.097: 100%|██████████| 1263/1263 [18:30<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 121, learning rate = 0.0028,                 previous best = 0.5730
pixAcc: 0.765, mIoU: 0.369: 100%|██████████| 125/125 [01:55<00:00,  1.09it/s]
Train loss: 1.078: 100%|██████████| 1263/1263 [18:32<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 122, learning rate = 0.0027,                 previous best = 0.5730
pixAcc: 0.761, mIoU: 0.375: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 1.059: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 123, learning rate = 0.0027,                 previous best = 0.5730
pixAcc: 0.767, mIoU: 0.380: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 1.045: 100%|██████████| 1263/1263 [18:45<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 124, learning rate = 0.0026,                 previous best = 0.5731
pixAcc: 0.765, mIoU: 0.376: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 1.054: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 125, learning rate = 0.0025,                 previous best = 0.5731
pixAcc: 0.762, mIoU: 0.375: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.034: 100%|██████████| 1263/1263 [18:43<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 126, learning rate = 0.0025,                 previous best = 0.5731
pixAcc: 0.764, mIoU: 0.384: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.034: 100%|██████████| 1263/1263 [18:31<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 127, learning rate = 0.0024,                 previous best = 0.5739
pixAcc: 0.765, mIoU: 0.379: 100%|██████████| 125/125 [01:56<00:00,  1.07it/s]
Train loss: 1.045: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 128, learning rate = 0.0023,                 previous best = 0.5739
pixAcc: 0.761, mIoU: 0.373: 100%|██████████| 125/125 [01:55<00:00,  1.08it/s]
Train loss: 1.032: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 129, learning rate = 0.0023,                 previous best = 0.5739
pixAcc: 0.763, mIoU: 0.382: 100%|██████████| 125/125 [01:51<00:00,  1.12it/s]
Train loss: 1.008: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 130, learning rate = 0.0022,                 previous best = 0.5739
pixAcc: 0.768, mIoU: 0.381: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 1.002: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 131, learning rate = 0.0022,                 previous best = 0.5743
pixAcc: 0.770, mIoU: 0.386: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 0.990: 100%|██████████| 1263/1263 [18:25<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 132, learning rate = 0.0021,                 previous best = 0.5781
pixAcc: 0.769, mIoU: 0.387: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 0.968: 100%|██████████| 1263/1263 [18:32<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 133, learning rate = 0.0020,                 previous best = 0.5781
pixAcc: 0.770, mIoU: 0.383: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.966: 100%|██████████| 1263/1263 [18:28<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 134, learning rate = 0.0019,                 previous best = 0.5781
pixAcc: 0.766, mIoU: 0.380: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.959: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 135, learning rate = 0.0019,                 previous best = 0.5781
pixAcc: 0.772, mIoU: 0.388: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.946: 100%|██████████| 1263/1263 [18:36<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 136, learning rate = 0.0018,                 previous best = 0.5802
pixAcc: 0.771, mIoU: 0.392: 100%|██████████| 125/125 [01:53<00:00,  1.11it/s]
Train loss: 0.946: 100%|██████████| 1263/1263 [18:35<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 137, learning rate = 0.0017,                 previous best = 0.5815
pixAcc: 0.773, mIoU: 0.387: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 0.933: 100%|██████████| 1263/1263 [18:46<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 138, learning rate = 0.0017,                 previous best = 0.5815
pixAcc: 0.771, mIoU: 0.385: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.926: 100%|██████████| 1263/1263 [18:30<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 139, learning rate = 0.0016,                 previous best = 0.5815
pixAcc: 0.767, mIoU: 0.391: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.919: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 140, learning rate = 0.0015,                 previous best = 0.5815
pixAcc: 0.771, mIoU: 0.389: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.914: 100%|██████████| 1263/1263 [18:28<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 141, learning rate = 0.0015,                 previous best = 0.5815
pixAcc: 0.773, mIoU: 0.392: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.900: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 142, learning rate = 0.0014,                 previous best = 0.5821
pixAcc: 0.774, mIoU: 0.390: 100%|██████████| 125/125 [01:54<00:00,  1.10it/s]
Train loss: 0.890: 100%|██████████| 1263/1263 [18:28<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 143, learning rate = 0.0013,                 previous best = 0.5821
pixAcc: 0.774, mIoU: 0.390: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.888: 100%|██████████| 1263/1263 [18:34<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 144, learning rate = 0.0013,                 previous best = 0.5821
pixAcc: 0.771, mIoU: 0.389: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.888: 100%|██████████| 1263/1263 [18:26<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 145, learning rate = 0.0012,                 previous best = 0.5821
pixAcc: 0.771, mIoU: 0.388: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 0.871: 100%|██████████| 1263/1263 [18:40<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 146, learning rate = 0.0011,                 previous best = 0.5821
pixAcc: 0.776, mIoU: 0.394: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 0.858: 100%|██████████| 1263/1263 [18:32<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 147, learning rate = 0.0010,                 previous best = 0.5849
pixAcc: 0.776, mIoU: 0.396: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.856: 100%|██████████| 1263/1263 [18:37<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 148, learning rate = 0.0010,                 previous best = 0.5861
pixAcc: 0.776, mIoU: 0.398: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.851: 100%|██████████| 1263/1263 [18:29<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 149, learning rate = 0.0009,                 previous best = 0.5867
pixAcc: 0.774, mIoU: 0.390: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.847: 100%|██████████| 1263/1263 [18:39<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 150, learning rate = 0.0008,                 previous best = 0.5867
pixAcc: 0.776, mIoU: 0.393: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 0.837: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 151, learning rate = 0.0008,                 previous best = 0.5867
pixAcc: 0.777, mIoU: 0.398: 100%|██████████| 125/125 [01:52<00:00,  1.12it/s]
Train loss: 0.833: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 152, learning rate = 0.0007,                 previous best = 0.5875
pixAcc: 0.775, mIoU: 0.396: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]
Train loss: 0.823: 100%|██████████| 1263/1263 [18:38<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 153, learning rate = 0.0006,                 previous best = 0.5875
pixAcc: 0.778, mIoU: 0.398: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.816: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 154, learning rate = 0.0005,                 previous best = 0.5877
pixAcc: 0.777, mIoU: 0.397: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.811: 100%|██████████| 1263/1263 [18:28<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 155, learning rate = 0.0004,                 previous best = 0.5877
pixAcc: 0.779, mIoU: 0.401: 100%|██████████| 125/125 [01:54<00:00,  1.09it/s]
Train loss: 0.803: 100%|██████████| 1263/1263 [18:44<00:00,  1.12it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 156, learning rate = 0.0004,                 previous best = 0.5899
pixAcc: 0.777, mIoU: 0.399: 100%|██████████| 125/125 [01:55<00:00,  1.08it/s]
Train loss: 0.803: 100%|██████████| 1263/1263 [18:34<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 157, learning rate = 0.0003,                 previous best = 0.5899
pixAcc: 0.778, mIoU: 0.400: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.796: 100%|██████████| 1263/1263 [18:41<00:00,  1.13it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 158, learning rate = 0.0002,                 previous best = 0.5899
pixAcc: 0.779, mIoU: 0.403: 100%|██████████| 125/125 [01:52<00:00,  1.11it/s]
Train loss: 0.793: 100%|██████████| 1263/1263 [18:25<00:00,  1.14it/s]
:   0%|          | 0/125 [00:00<?, ?it/s]
=>Epoches 159, learning rate = 0.0001,                 previous best = 0.5912
pixAcc: 0.780, mIoU: 0.402: 100%|██████████| 125/125 [01:53<00:00,  1.10it/s]