Namespace(aux=False, aux_weight=0.5, backbone='resnet50', base_size=2048, batch_size=16, checkname='default', crop_size=768, ctx=[gpu(0), gpu(1), gpu(2), gpu(3), gpu(4), gpu(5), gpu(6), gpu(7)], dataset='citys', dtype='float32', epochs=240, eval=False, kvstore='device', log_interval=20, logging_file='train.log', lr=0.01, mode=None, model='icnet', model_zoo=None, momentum=0.9, ngpus=8, no_cuda=False, no_val=False, no_wd=False, norm_kwargs={'num_devices': 8}, norm_layer=<class 'mxnet.gluon.contrib.nn.basic_layers.SyncBatchNorm'>, resume=None, save_dir='runs/citys/icnet/resnet50/', start_epoch=0, syncbn=True, test_batch_size=16, train_split='train', weight_decay=0.0001, workers=32)
ICNet(
  (conv1): HybridSequential(
    (0): Conv2D(3 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_syncbatchnorm0_', in_channels=64)
    (2): Activation(relu)
    (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (4): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_syncbatchnorm1_', in_channels=64)
    (5): Activation(relu)
    (6): Conv2D(64 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  )
  (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_syncbatchnorm2_', in_channels=128)
  (relu): Activation(relu)
  (maxpool): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
  (layer1): HybridSequential(
    (0): BottleneckV1b(
      (conv1): Conv2D(128 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm0_', in_channels=64)
      (relu1): Activation(relu)
      (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm1_', in_channels=64)
      (relu2): Activation(relu)
      (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm2_', in_channels=256)
      (relu3): Activation(relu)
      (downsample): HybridSequential(
        (0): Conv2D(128 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_down1_syncbatchnorm0_', in_channels=256)
      )
    )
    (1): BottleneckV1b(
      (conv1): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm3_', in_channels=64)
      (relu1): Activation(relu)
      (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm4_', in_channels=64)
      (relu2): Activation(relu)
      (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm5_', in_channels=256)
      (relu3): Activation(relu)
    )
    (2): BottleneckV1b(
      (conv1): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm6_', in_channels=64)
      (relu1): Activation(relu)
      (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm7_', in_channels=64)
      (relu2): Activation(relu)
      (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers1_syncbatchnorm8_', in_channels=256)
      (relu3): Activation(relu)
    )
  )
  (layer2): HybridSequential(
    (0): BottleneckV1b(
      (conv1): Conv2D(256 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm0_', in_channels=128)
      (relu1): Activation(relu)
      (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm1_', in_channels=128)
      (relu2): Activation(relu)
      (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm2_', in_channels=512)
      (relu3): Activation(relu)
      (downsample): HybridSequential(
        (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_down2_syncbatchnorm0_', in_channels=512)
      )
    )
    (1): BottleneckV1b(
      (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm3_', in_channels=128)
      (relu1): Activation(relu)
      (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm4_', in_channels=128)
      (relu2): Activation(relu)
      (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm5_', in_channels=512)
      (relu3): Activation(relu)
    )
    (2): BottleneckV1b(
      (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm6_', in_channels=128)
      (relu1): Activation(relu)
      (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm7_', in_channels=128)
      (relu2): Activation(relu)
      (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm8_', in_channels=512)
      (relu3): Activation(relu)
    )
    (3): BottleneckV1b(
      (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm9_', in_channels=128)
      (relu1): Activation(relu)
      (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm10_', in_channels=128)
      (relu2): Activation(relu)
      (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers2_syncbatchnorm11_', in_channels=512)
      (relu3): Activation(relu)
    )
  )
  (layer3): HybridSequential(
    (0): BottleneckV1b(
      (conv1): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm0_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm1_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm2_', in_channels=1024)
      (relu3): Activation(relu)
      (downsample): HybridSequential(
        (0): Conv2D(512 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_down3_syncbatchnorm0_', in_channels=1024)
      )
    )
    (1): BottleneckV1b(
      (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm3_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm4_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm5_', in_channels=1024)
      (relu3): Activation(relu)
    )
    (2): BottleneckV1b(
      (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm6_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm7_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm8_', in_channels=1024)
      (relu3): Activation(relu)
    )
    (3): BottleneckV1b(
      (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm9_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm10_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm11_', in_channels=1024)
      (relu3): Activation(relu)
    )
    (4): BottleneckV1b(
      (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm12_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm13_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm14_', in_channels=1024)
      (relu3): Activation(relu)
    )
    (5): BottleneckV1b(
      (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm15_', in_channels=256)
      (relu1): Activation(relu)
      (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm16_', in_channels=256)
      (relu2): Activation(relu)
      (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers3_syncbatchnorm17_', in_channels=1024)
      (relu3): Activation(relu)
    )
  )
  (layer4): HybridSequential(
    (0): BottleneckV1b(
      (conv1): Conv2D(1024 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm0_', in_channels=512)
      (relu1): Activation(relu)
      (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm1_', in_channels=512)
      (relu2): Activation(relu)
      (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm2_', in_channels=2048)
      (relu3): Activation(relu)
      (downsample): HybridSequential(
        (0): Conv2D(1024 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_down4_syncbatchnorm0_', in_channels=2048)
      )
    )
    (1): BottleneckV1b(
      (conv1): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm3_', in_channels=512)
      (relu1): Activation(relu)
      (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm4_', in_channels=512)
      (relu2): Activation(relu)
      (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm5_', in_channels=2048)
      (relu3): Activation(relu)
    )
    (2): BottleneckV1b(
      (conv1): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm6_', in_channels=512)
      (relu1): Activation(relu)
      (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
      (bn2): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm7_', in_channels=512)
      (relu2): Activation(relu)
      (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_resnetv1s_layers4_syncbatchnorm8_', in_channels=2048)
      (relu3): Activation(relu)
    )
  )
  (conv_sub1): HybridSequential(
    (0): ConvBnRelu(
      (conv): Conv2D(3 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_hybridsequential0_convbnrelu0_syncbatchnorm0_', in_channels=32)
      (relu): Activation(relu)
    )
    (1): ConvBnRelu(
      (conv): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_hybridsequential0_convbnrelu1_syncbatchnorm0_', in_channels=32)
      (relu): Activation(relu)
    )
    (2): ConvBnRelu(
      (conv): Conv2D(32 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_hybridsequential0_convbnrelu2_syncbatchnorm0_', in_channels=64)
      (relu): Activation(relu)
    )
  )
  (psp_head): HybridSequential(
    (0): _PyramidPooling(
      (conv1): HybridSequential(
        (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0__pyramidpooling0_hybridsequential0_syncbatchnorm0_', in_channels=512)
        (2): Activation(relu)
      )
      (conv2): HybridSequential(
        (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0__pyramidpooling0_hybridsequential1_syncbatchnorm0_', in_channels=512)
        (2): Activation(relu)
      )
      (conv3): HybridSequential(
        (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0__pyramidpooling0_hybridsequential2_syncbatchnorm0_', in_channels=512)
        (2): Activation(relu)
      )
      (conv4): HybridSequential(
        (0): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0__pyramidpooling0_hybridsequential3_syncbatchnorm0_', in_channels=512)
        (2): Activation(relu)
      )
    )
    (1): HybridSequential(
      (0): Conv2D(4096 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0__psphead0_syncbatchnorm0_', in_channels=512)
      (2): Activation(relu)
      (3): Dropout(p = 0.1, axes=())
    )
  )
  (head): _ICHead(
    (cff_12): CascadeFeatureFusion(
      (conv_low): HybridSequential(
        (0): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_cascadefeaturefusion0_hybridsequential0_syncbatchnorm0_', in_channels=128)
      )
      (conv_hign): HybridSequential(
        (0): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_cascadefeaturefusion0_hybridsequential1_syncbatchnorm0_', in_channels=128)
      )
      (conv_low_cls): Conv2D(128 -> 19, kernel_size=(1, 1), stride=(1, 1), bias=False)
    )
    (cff_24): CascadeFeatureFusion(
      (conv_low): HybridSequential(
        (0): Conv2D(256 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_cascadefeaturefusion1_hybridsequential0_syncbatchnorm0_', in_channels=128)
      )
      (conv_hign): HybridSequential(
        (0): Conv2D(256 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_cascadefeaturefusion1_hybridsequential1_syncbatchnorm0_', in_channels=128)
      )
      (conv_low_cls): Conv2D(128 -> 19, kernel_size=(1, 1), stride=(1, 1), bias=False)
    )
    (conv_cls): Conv2D(128 -> 19, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
  (conv_sub4): ConvBnRelu(
    (conv): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_convbnrelu0_syncbatchnorm0_', in_channels=256)
    (relu): Activation(relu)
  )
  (conv_sub2): ConvBnRelu(
    (conv): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn): SyncBatchNorm(eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, ndev=8, key='icnet0_convbnrelu1_syncbatchnorm0_', in_channels=256)
    (relu): Activation(relu)
  )
)
Starting Epoch: 0
Total Epochs: 240
Epoch 0 iteration 0020/0185: training loss 3.792
Epoch 0 iteration 0040/0185: training loss 3.107
Epoch 0 iteration 0060/0185: training loss 2.772
Epoch 0 iteration 0080/0185: training loss 2.578
Epoch 0 iteration 0100/0185: training loss 2.450
Epoch 0 iteration 0120/0185: training loss 2.324
Epoch 0 iteration 0140/0186: training loss 2.239
Epoch 0 iteration 0160/0186: training loss 2.160
Epoch 0 iteration 0180/0186: training loss 2.083
Epoch 0 validation pixAcc: 0.885, mIoU: 0.276
Epoch 1 iteration 0020/0185: training loss 1.440
Epoch 1 iteration 0040/0185: training loss 1.484
Epoch 1 iteration 0060/0185: training loss 1.415
Epoch 1 iteration 0080/0185: training loss 1.383
Epoch 1 iteration 0100/0185: training loss 1.346
Epoch 1 iteration 0120/0185: training loss 1.327
Epoch 1 iteration 0140/0186: training loss 1.323
Epoch 1 iteration 0160/0186: training loss 1.318
Epoch 1 iteration 0180/0186: training loss 1.304
Epoch 1 validation pixAcc: 0.876, mIoU: 0.294
Epoch 2 iteration 0020/0185: training loss 1.302
Epoch 2 iteration 0040/0185: training loss 1.272
Epoch 2 iteration 0060/0185: training loss 1.210
Epoch 2 iteration 0080/0185: training loss 1.186
Epoch 2 iteration 0100/0185: training loss 1.171
Epoch 2 iteration 0120/0185: training loss 1.162
Epoch 2 iteration 0140/0186: training loss 1.151
Epoch 2 iteration 0160/0186: training loss 1.137
Epoch 2 iteration 0180/0186: training loss 1.115
Epoch 2 validation pixAcc: 0.895, mIoU: 0.359
Epoch 3 iteration 0020/0185: training loss 1.055
Epoch 3 iteration 0040/0185: training loss 0.983
Epoch 3 iteration 0060/0185: training loss 1.012
Epoch 3 iteration 0080/0185: training loss 1.012
Epoch 3 iteration 0100/0185: training loss 0.980
Epoch 3 iteration 0120/0185: training loss 0.959
Epoch 3 iteration 0140/0186: training loss 0.943
Epoch 3 iteration 0160/0186: training loss 0.938
Epoch 3 iteration 0180/0186: training loss 0.941
Epoch 3 validation pixAcc: 0.908, mIoU: 0.361
Epoch 4 iteration 0020/0185: training loss 0.868
Epoch 4 iteration 0040/0185: training loss 0.851
Epoch 4 iteration 0060/0185: training loss 0.852
Epoch 4 iteration 0080/0185: training loss 0.878
Epoch 4 iteration 0100/0185: training loss 0.906
Epoch 4 iteration 0120/0185: training loss 0.924
Epoch 4 iteration 0140/0186: training loss 0.920
Epoch 4 iteration 0160/0186: training loss 0.919
Epoch 4 iteration 0180/0186: training loss 0.930
Epoch 4 validation pixAcc: 0.909, mIoU: 0.371
Epoch 5 iteration 0020/0185: training loss 0.886
Epoch 5 iteration 0040/0185: training loss 0.871
Epoch 5 iteration 0060/0185: training loss 0.849
Epoch 5 iteration 0080/0185: training loss 0.854
Epoch 5 iteration 0100/0185: training loss 0.842
Epoch 5 iteration 0120/0185: training loss 0.833
Epoch 5 iteration 0140/0186: training loss 0.821
Epoch 5 iteration 0160/0186: training loss 0.820
Epoch 5 iteration 0180/0186: training loss 0.816
Epoch 5 validation pixAcc: 0.917, mIoU: 0.430
Epoch 6 iteration 0020/0185: training loss 0.728
Epoch 6 iteration 0040/0185: training loss 0.746
Epoch 6 iteration 0060/0185: training loss 0.768
Epoch 6 iteration 0080/0185: training loss 0.785
Epoch 6 iteration 0100/0185: training loss 0.779
Epoch 6 iteration 0120/0185: training loss 0.799
Epoch 6 iteration 0140/0186: training loss 0.813
Epoch 6 iteration 0160/0186: training loss 0.813
Epoch 6 iteration 0180/0186: training loss 0.815
Epoch 6 validation pixAcc: 0.891, mIoU: 0.381
Epoch 7 iteration 0020/0185: training loss 0.775
Epoch 7 iteration 0040/0185: training loss 0.778
Epoch 7 iteration 0060/0185: training loss 0.781
Epoch 7 iteration 0080/0185: training loss 0.802
Epoch 7 iteration 0100/0185: training loss 0.824
Epoch 7 iteration 0120/0185: training loss 0.833
Epoch 7 iteration 0140/0186: training loss 0.833
Epoch 7 iteration 0160/0186: training loss 0.836
Epoch 7 iteration 0180/0186: training loss 0.826
Epoch 7 validation pixAcc: 0.920, mIoU: 0.456
Epoch 8 iteration 0020/0185: training loss 0.943
Epoch 8 iteration 0040/0185: training loss 0.857
Epoch 8 iteration 0060/0185: training loss 0.832
Epoch 8 iteration 0080/0185: training loss 0.796
Epoch 8 iteration 0100/0185: training loss 0.797
Epoch 8 iteration 0120/0185: training loss 0.795
Epoch 8 iteration 0140/0186: training loss 0.787
Epoch 8 iteration 0160/0186: training loss 0.789
Epoch 8 iteration 0180/0186: training loss 0.798
Epoch 8 validation pixAcc: 0.919, mIoU: 0.410
Epoch 9 iteration 0020/0185: training loss 0.796
Epoch 9 iteration 0040/0185: training loss 0.803
Epoch 9 iteration 0060/0185: training loss 0.769
Epoch 9 iteration 0080/0185: training loss 0.749
Epoch 9 iteration 0100/0185: training loss 0.730
Epoch 9 iteration 0120/0185: training loss 0.731
Epoch 9 iteration 0140/0186: training loss 0.741
Epoch 9 iteration 0160/0186: training loss 0.737
Epoch 9 iteration 0180/0186: training loss 0.738
Epoch 9 validation pixAcc: 0.921, mIoU: 0.456
Epoch 10 iteration 0020/0185: training loss 0.733
Epoch 10 iteration 0040/0185: training loss 0.772
Epoch 10 iteration 0060/0185: training loss 0.827
Epoch 10 iteration 0080/0185: training loss 0.899
Epoch 10 iteration 0100/0185: training loss 0.911
Epoch 10 iteration 0120/0185: training loss 0.896
Epoch 10 iteration 0140/0186: training loss 0.888
Epoch 10 iteration 0160/0186: training loss 0.883
Epoch 10 iteration 0180/0186: training loss 0.871
Epoch 10 validation pixAcc: 0.917, mIoU: 0.444
Epoch 11 iteration 0020/0185: training loss 0.844
Epoch 11 iteration 0040/0185: training loss 0.882
Epoch 11 iteration 0060/0185: training loss 0.896
Epoch 11 iteration 0080/0185: training loss 0.895
Epoch 11 iteration 0100/0185: training loss 0.873
Epoch 11 iteration 0120/0185: training loss 0.863
Epoch 11 iteration 0140/0186: training loss 0.845
Epoch 11 iteration 0160/0186: training loss 0.844
Epoch 11 iteration 0180/0186: training loss 0.828
Epoch 11 validation pixAcc: 0.918, mIoU: 0.464
Epoch 12 iteration 0020/0185: training loss 0.708
Epoch 12 iteration 0040/0185: training loss 0.717
Epoch 12 iteration 0060/0185: training loss 0.725
Epoch 12 iteration 0080/0185: training loss 0.723
Epoch 12 iteration 0100/0185: training loss 0.716
Epoch 12 iteration 0120/0185: training loss 0.705
Epoch 12 iteration 0140/0186: training loss 0.697
Epoch 12 iteration 0160/0186: training loss 0.703
Epoch 12 iteration 0180/0186: training loss 0.720
Epoch 12 validation pixAcc: 0.919, mIoU: 0.435
Epoch 13 iteration 0020/0185: training loss 0.715
Epoch 13 iteration 0040/0185: training loss 0.708
Epoch 13 iteration 0060/0185: training loss 0.709
Epoch 13 iteration 0080/0185: training loss 0.705
Epoch 13 iteration 0100/0185: training loss 0.705
Epoch 13 iteration 0120/0185: training loss 0.701
Epoch 13 iteration 0140/0186: training loss 0.685
Epoch 13 iteration 0160/0186: training loss 0.688
Epoch 13 iteration 0180/0186: training loss 0.685
Epoch 13 validation pixAcc: 0.929, mIoU: 0.499
Epoch 14 iteration 0020/0185: training loss 0.637
Epoch 14 iteration 0040/0185: training loss 0.616
Epoch 14 iteration 0060/0185: training loss 0.646
Epoch 14 iteration 0080/0185: training loss 0.664
Epoch 14 iteration 0100/0185: training loss 0.673
Epoch 14 iteration 0120/0185: training loss 0.662
Epoch 14 iteration 0140/0186: training loss 0.685
Epoch 14 iteration 0160/0186: training loss 0.712
Epoch 14 iteration 0180/0186: training loss 0.711
Epoch 14 validation pixAcc: 0.922, mIoU: 0.478
Epoch 15 iteration 0020/0185: training loss 0.728
Epoch 15 iteration 0040/0185: training loss 0.727
Epoch 15 iteration 0060/0185: training loss 0.728
Epoch 15 iteration 0080/0185: training loss 0.714
Epoch 15 iteration 0100/0185: training loss 0.706
Epoch 15 iteration 0120/0185: training loss 0.697
Epoch 15 iteration 0140/0185: training loss 0.693
Epoch 15 iteration 0160/0185: training loss 0.683
Epoch 15 iteration 0180/0185: training loss 0.679
Epoch 15 validation pixAcc: 0.929, mIoU: 0.512
Epoch 16 iteration 0020/0185: training loss 0.645
Epoch 16 iteration 0040/0185: training loss 0.637
Epoch 16 iteration 0060/0185: training loss 0.637
Epoch 16 iteration 0080/0185: training loss 0.650
Epoch 16 iteration 0100/0185: training loss 0.642
Epoch 16 iteration 0120/0185: training loss 0.632
Epoch 16 iteration 0140/0186: training loss 0.631
Epoch 16 iteration 0160/0186: training loss 0.642
Epoch 16 iteration 0180/0186: training loss 0.645
Epoch 16 validation pixAcc: 0.924, mIoU: 0.469
Epoch 17 iteration 0020/0185: training loss 0.643
Epoch 17 iteration 0040/0185: training loss 0.672
Epoch 17 iteration 0060/0185: training loss 0.659
Epoch 17 iteration 0080/0185: training loss 0.656
Epoch 17 iteration 0100/0185: training loss 0.663
Epoch 17 iteration 0120/0185: training loss 0.669
Epoch 17 iteration 0140/0186: training loss 0.671
Epoch 17 iteration 0160/0186: training loss 0.675
Epoch 17 iteration 0180/0186: training loss 0.669
Epoch 17 validation pixAcc: 0.921, mIoU: 0.469
Epoch 18 iteration 0020/0185: training loss 0.634
Epoch 18 iteration 0040/0185: training loss 0.631
Epoch 18 iteration 0060/0185: training loss 0.624
Epoch 18 iteration 0080/0185: training loss 0.636
Epoch 18 iteration 0100/0185: training loss 0.643
Epoch 18 iteration 0120/0185: training loss 0.634
Epoch 18 iteration 0140/0186: training loss 0.626
Epoch 18 iteration 0160/0186: training loss 0.620
Epoch 18 iteration 0180/0186: training loss 0.621
Epoch 18 validation pixAcc: 0.932, mIoU: 0.510
Epoch 19 iteration 0020/0185: training loss 0.593
Epoch 19 iteration 0040/0185: training loss 0.580
Epoch 19 iteration 0060/0185: training loss 0.633
Epoch 19 iteration 0080/0185: training loss 0.641
Epoch 19 iteration 0100/0185: training loss 0.637
Epoch 19 iteration 0120/0185: training loss 0.633
Epoch 19 iteration 0140/0186: training loss 0.631
Epoch 19 iteration 0160/0186: training loss 0.628
Epoch 19 iteration 0180/0186: training loss 0.628
Epoch 19 validation pixAcc: 0.924, mIoU: 0.501
Epoch 20 iteration 0020/0185: training loss 0.619
Epoch 20 iteration 0040/0185: training loss 0.608
Epoch 20 iteration 0060/0185: training loss 0.607
Epoch 20 iteration 0080/0185: training loss 0.617
Epoch 20 iteration 0100/0185: training loss 0.631
Epoch 20 iteration 0120/0185: training loss 0.629
Epoch 20 iteration 0140/0186: training loss 0.623
Epoch 20 iteration 0160/0186: training loss 0.625
Epoch 20 iteration 0180/0186: training loss 0.634
Epoch 20 validation pixAcc: 0.927, mIoU: 0.520
Epoch 21 iteration 0020/0185: training loss 0.668
Epoch 21 iteration 0040/0185: training loss 0.652
Epoch 21 iteration 0060/0185: training loss 0.654
Epoch 21 iteration 0080/0185: training loss 0.642
Epoch 21 iteration 0100/0185: training loss 0.641
Epoch 21 iteration 0120/0185: training loss 0.634
Epoch 21 iteration 0140/0186: training loss 0.636
Epoch 21 iteration 0160/0186: training loss 0.642
Epoch 21 iteration 0180/0186: training loss 0.662
Epoch 21 validation pixAcc: 0.918, mIoU: 0.479
Epoch 22 iteration 0020/0185: training loss 0.637
Epoch 22 iteration 0040/0185: training loss 0.609
Epoch 22 iteration 0060/0185: training loss 0.629
Epoch 22 iteration 0080/0185: training loss 0.644
Epoch 22 iteration 0100/0185: training loss 0.656
Epoch 22 iteration 0120/0185: training loss 0.655
Epoch 22 iteration 0140/0186: training loss 0.645
Epoch 22 iteration 0160/0186: training loss 0.636
Epoch 22 iteration 0180/0186: training loss 0.632
Epoch 22 validation pixAcc: 0.927, mIoU: 0.523
Epoch 23 iteration 0020/0185: training loss 0.573
Epoch 23 iteration 0040/0185: training loss 0.555
Epoch 23 iteration 0060/0185: training loss 0.582
Epoch 23 iteration 0080/0185: training loss 0.602
Epoch 23 iteration 0100/0185: training loss 0.606
Epoch 23 iteration 0120/0185: training loss 0.608
Epoch 23 iteration 0140/0186: training loss 0.603
Epoch 23 iteration 0160/0186: training loss 0.600
Epoch 23 iteration 0180/0186: training loss 0.596
Epoch 23 validation pixAcc: 0.933, mIoU: 0.544
Epoch 24 iteration 0020/0185: training loss 0.524
Epoch 24 iteration 0040/0185: training loss 0.543
Epoch 24 iteration 0060/0185: training loss 0.569
Epoch 24 iteration 0080/0185: training loss 0.583
Epoch 24 iteration 0100/0185: training loss 0.589
Epoch 24 iteration 0120/0185: training loss 0.601
Epoch 24 iteration 0140/0186: training loss 0.602
Epoch 24 iteration 0160/0186: training loss 0.599
Epoch 24 iteration 0180/0186: training loss 0.594
Epoch 24 validation pixAcc: 0.934, mIoU: 0.538
Epoch 25 iteration 0020/0185: training loss 0.567
Epoch 25 iteration 0040/0185: training loss 0.555
Epoch 25 iteration 0060/0185: training loss 0.552
Epoch 25 iteration 0080/0185: training loss 0.549
Epoch 25 iteration 0100/0185: training loss 0.549
Epoch 25 iteration 0120/0185: training loss 0.549
Epoch 25 iteration 0140/0186: training loss 0.550
Epoch 25 iteration 0160/0186: training loss 0.548
Epoch 25 iteration 0180/0186: training loss 0.548
Epoch 25 validation pixAcc: 0.933, mIoU: 0.535
Epoch 26 iteration 0020/0185: training loss 0.562
Epoch 26 iteration 0040/0185: training loss 0.613
Epoch 26 iteration 0060/0185: training loss 0.625
Epoch 26 iteration 0080/0185: training loss 0.651
Epoch 26 iteration 0100/0185: training loss 0.655
Epoch 26 iteration 0120/0185: training loss 0.659
Epoch 26 iteration 0140/0186: training loss 0.660
Epoch 26 iteration 0160/0186: training loss 0.659
Epoch 26 iteration 0180/0186: training loss 0.656
Epoch 26 validation pixAcc: 0.932, mIoU: 0.528
Epoch 27 iteration 0020/0185: training loss 0.582
Epoch 27 iteration 0040/0185: training loss 0.574
Epoch 27 iteration 0060/0185: training loss 0.568
Epoch 27 iteration 0080/0185: training loss 0.564
Epoch 27 iteration 0100/0185: training loss 0.564
Epoch 27 iteration 0120/0185: training loss 0.562
Epoch 27 iteration 0140/0186: training loss 0.555
Epoch 27 iteration 0160/0186: training loss 0.552
Epoch 27 iteration 0180/0186: training loss 0.548
Epoch 27 validation pixAcc: 0.932, mIoU: 0.536
Epoch 28 iteration 0020/0185: training loss 0.509
Epoch 28 iteration 0040/0185: training loss 0.508
Epoch 28 iteration 0060/0185: training loss 0.524
Epoch 28 iteration 0080/0185: training loss 0.529
Epoch 28 iteration 0100/0185: training loss 0.533
Epoch 28 iteration 0120/0185: training loss 0.533
Epoch 28 iteration 0140/0186: training loss 0.532
Epoch 28 iteration 0160/0186: training loss 0.533
Epoch 28 iteration 0180/0186: training loss 0.538
Epoch 28 validation pixAcc: 0.933, mIoU: 0.528
Epoch 29 iteration 0020/0185: training loss 0.593
Epoch 29 iteration 0040/0185: training loss 0.596
Epoch 29 iteration 0060/0185: training loss 0.578
Epoch 29 iteration 0080/0185: training loss 0.563
Epoch 29 iteration 0100/0185: training loss 0.555
Epoch 29 iteration 0120/0185: training loss 0.550
Epoch 29 iteration 0140/0186: training loss 0.549
Epoch 29 iteration 0160/0186: training loss 0.558
Epoch 29 iteration 0180/0186: training loss 0.558
Epoch 29 validation pixAcc: 0.933, mIoU: 0.542
Epoch 30 iteration 0020/0185: training loss 0.540
Epoch 30 iteration 0040/0185: training loss 0.548
Epoch 30 iteration 0060/0185: training loss 0.553
Epoch 30 iteration 0080/0185: training loss 0.561
Epoch 30 iteration 0100/0185: training loss 0.557
Epoch 30 iteration 0120/0185: training loss 0.562
Epoch 30 iteration 0140/0186: training loss 0.567
Epoch 30 iteration 0160/0186: training loss 0.572
Epoch 30 iteration 0180/0186: training loss 0.574
Epoch 30 validation pixAcc: 0.933, mIoU: 0.547
Epoch 31 iteration 0020/0185: training loss 0.534
Epoch 31 iteration 0040/0185: training loss 0.526
Epoch 31 iteration 0060/0185: training loss 0.523
Epoch 31 iteration 0080/0185: training loss 0.521
Epoch 31 iteration 0100/0185: training loss 0.539
Epoch 31 iteration 0120/0185: training loss 0.538
Epoch 31 iteration 0140/0185: training loss 0.540
Epoch 31 iteration 0160/0185: training loss 0.540
Epoch 31 iteration 0180/0185: training loss 0.536
Epoch 31 validation pixAcc: 0.935, mIoU: 0.552
Epoch 32 iteration 0020/0185: training loss 0.536
Epoch 32 iteration 0040/0185: training loss 0.568
Epoch 32 iteration 0060/0185: training loss 0.592
Epoch 32 iteration 0080/0185: training loss 0.603
Epoch 32 iteration 0100/0185: training loss 0.589
Epoch 32 iteration 0120/0185: training loss 0.587
Epoch 32 iteration 0140/0186: training loss 0.584
Epoch 32 iteration 0160/0186: training loss 0.587
Epoch 32 iteration 0180/0186: training loss 0.581
Epoch 32 validation pixAcc: 0.934, mIoU: 0.527
Epoch 33 iteration 0020/0185: training loss 0.546
Epoch 33 iteration 0040/0185: training loss 0.533
Epoch 33 iteration 0060/0185: training loss 0.546
Epoch 33 iteration 0080/0185: training loss 0.536
Epoch 33 iteration 0100/0185: training loss 0.525
Epoch 33 iteration 0120/0185: training loss 0.518
Epoch 33 iteration 0140/0186: training loss 0.516
Epoch 33 iteration 0160/0186: training loss 0.516
Epoch 33 iteration 0180/0186: training loss 0.523
Epoch 33 validation pixAcc: 0.934, mIoU: 0.533
Epoch 34 iteration 0020/0185: training loss 0.522
Epoch 34 iteration 0040/0185: training loss 0.518
Epoch 34 iteration 0060/0185: training loss 0.527
Epoch 34 iteration 0080/0185: training loss 0.529
Epoch 34 iteration 0100/0185: training loss 0.528
Epoch 34 iteration 0120/0185: training loss 0.527
Epoch 34 iteration 0140/0186: training loss 0.529
Epoch 34 iteration 0160/0186: training loss 0.530
Epoch 34 iteration 0180/0186: training loss 0.528
Epoch 34 validation pixAcc: 0.937, mIoU: 0.569
Epoch 35 iteration 0020/0185: training loss 0.530
Epoch 35 iteration 0040/0185: training loss 0.512
Epoch 35 iteration 0060/0185: training loss 0.495
Epoch 35 iteration 0080/0185: training loss 0.495
Epoch 35 iteration 0100/0185: training loss 0.505
Epoch 35 iteration 0120/0185: training loss 0.511
Epoch 35 iteration 0140/0186: training loss 0.514
Epoch 35 iteration 0160/0186: training loss 0.519
Epoch 35 iteration 0180/0186: training loss 0.522
Epoch 35 validation pixAcc: 0.936, mIoU: 0.550
Epoch 36 iteration 0020/0185: training loss 0.513
Epoch 36 iteration 0040/0185: training loss 0.486
Epoch 36 iteration 0060/0185: training loss 0.493
Epoch 36 iteration 0080/0185: training loss 0.491
Epoch 36 iteration 0100/0185: training loss 0.504
Epoch 36 iteration 0120/0185: training loss 0.500
Epoch 36 iteration 0140/0186: training loss 0.503
Epoch 36 iteration 0160/0186: training loss 0.507
Epoch 36 iteration 0180/0186: training loss 0.508
Epoch 36 validation pixAcc: 0.934, mIoU: 0.544
Epoch 37 iteration 0020/0185: training loss 0.535
Epoch 37 iteration 0040/0185: training loss 0.519
Epoch 37 iteration 0060/0185: training loss 0.530
Epoch 37 iteration 0080/0185: training loss 0.532
Epoch 37 iteration 0100/0185: training loss 0.533
Epoch 37 iteration 0120/0185: training loss 0.524
Epoch 37 iteration 0140/0186: training loss 0.520
Epoch 37 iteration 0160/0186: training loss 0.514
Epoch 37 iteration 0180/0186: training loss 0.511
Epoch 37 validation pixAcc: 0.939, mIoU: 0.597
Epoch 38 iteration 0020/0185: training loss 0.466
Epoch 38 iteration 0040/0185: training loss 0.598
Epoch 38 iteration 0060/0185: training loss 0.671
Epoch 38 iteration 0080/0185: training loss 0.682
Epoch 38 iteration 0100/0185: training loss 0.670
Epoch 38 iteration 0120/0185: training loss 0.656
Epoch 38 iteration 0140/0186: training loss 0.647
Epoch 38 iteration 0160/0186: training loss 0.648
Epoch 38 iteration 0180/0186: training loss 0.643
Epoch 38 validation pixAcc: 0.934, mIoU: 0.558
Epoch 39 iteration 0020/0185: training loss 0.563
Epoch 39 iteration 0040/0185: training loss 0.547
Epoch 39 iteration 0060/0185: training loss 0.538
Epoch 39 iteration 0080/0185: training loss 0.551
Epoch 39 iteration 0100/0185: training loss 0.545
Epoch 39 iteration 0120/0185: training loss 0.557
Epoch 39 iteration 0140/0186: training loss 0.555
Epoch 39 iteration 0160/0186: training loss 0.548
Epoch 39 iteration 0180/0186: training loss 0.540
Epoch 39 validation pixAcc: 0.939, mIoU: 0.589
Epoch 40 iteration 0020/0185: training loss 0.541
Epoch 40 iteration 0040/0185: training loss 0.570
Epoch 40 iteration 0060/0185: training loss 0.572
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Epoch 40 iteration 0140/0186: training loss 0.557
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Epoch 40 iteration 0180/0186: training loss 0.546
Epoch 40 validation pixAcc: 0.932, mIoU: 0.531
Epoch 41 iteration 0020/0185: training loss 0.548
Epoch 41 iteration 0040/0185: training loss 0.566
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Epoch 41 validation pixAcc: 0.929, mIoU: 0.518
Epoch 42 iteration 0020/0185: training loss 0.555
Epoch 42 iteration 0040/0185: training loss 0.548
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Epoch 42 iteration 0100/0185: training loss 0.533
Epoch 42 iteration 0120/0185: training loss 0.531
Epoch 42 iteration 0140/0186: training loss 0.531
Epoch 42 iteration 0160/0186: training loss 0.525
Epoch 42 iteration 0180/0186: training loss 0.525
Epoch 42 validation pixAcc: 0.936, mIoU: 0.581
Epoch 43 iteration 0020/0185: training loss 0.465
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Epoch 43 iteration 0060/0185: training loss 0.486
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Epoch 43 iteration 0140/0186: training loss 0.498
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Epoch 43 iteration 0180/0186: training loss 0.496
Epoch 43 validation pixAcc: 0.939, mIoU: 0.589
Epoch 44 iteration 0020/0185: training loss 0.499
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Epoch 44 iteration 0180/0186: training loss 0.492
Epoch 44 validation pixAcc: 0.940, mIoU: 0.577
Epoch 45 iteration 0020/0185: training loss 0.475
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Epoch 45 iteration 0180/0186: training loss 0.492
Epoch 45 validation pixAcc: 0.935, mIoU: 0.533
Epoch 46 iteration 0020/0185: training loss 0.501
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Epoch 46 iteration 0120/0185: training loss 0.516
Epoch 46 iteration 0140/0186: training loss 0.514
Epoch 46 iteration 0160/0186: training loss 0.511
Epoch 46 iteration 0180/0186: training loss 0.509
Epoch 46 validation pixAcc: 0.939, mIoU: 0.577
Epoch 47 iteration 0020/0185: training loss 0.516
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Epoch 47 iteration 0140/0185: training loss 0.505
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Epoch 47 iteration 0180/0185: training loss 0.511
Epoch 47 validation pixAcc: 0.938, mIoU: 0.577
Epoch 48 iteration 0020/0185: training loss 0.570
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Epoch 48 iteration 0060/0185: training loss 0.539
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Epoch 48 iteration 0140/0186: training loss 0.509
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Epoch 48 iteration 0180/0186: training loss 0.501
Epoch 48 validation pixAcc: 0.939, mIoU: 0.591
Epoch 49 iteration 0020/0185: training loss 0.456
Epoch 49 iteration 0040/0185: training loss 0.467
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Epoch 49 iteration 0140/0186: training loss 0.474
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Epoch 49 iteration 0180/0186: training loss 0.471
Epoch 49 validation pixAcc: 0.941, mIoU: 0.598
Epoch 50 iteration 0020/0185: training loss 0.479
Epoch 50 iteration 0040/0185: training loss 0.465
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Epoch 50 iteration 0080/0185: training loss 0.455
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Epoch 50 iteration 0140/0186: training loss 0.471
Epoch 50 iteration 0160/0186: training loss 0.475
Epoch 50 iteration 0180/0186: training loss 0.479
Epoch 50 validation pixAcc: 0.938, mIoU: 0.572
Epoch 51 iteration 0020/0185: training loss 0.513
Epoch 51 iteration 0040/0185: training loss 0.497
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Epoch 51 iteration 0080/0185: training loss 0.487
Epoch 51 iteration 0100/0185: training loss 0.480
Epoch 51 iteration 0120/0185: training loss 0.477
Epoch 51 iteration 0140/0186: training loss 0.471
Epoch 51 iteration 0160/0186: training loss 0.470
Epoch 51 iteration 0180/0186: training loss 0.470
Epoch 51 validation pixAcc: 0.940, mIoU: 0.579
Epoch 52 iteration 0020/0185: training loss 0.476
Epoch 52 iteration 0040/0185: training loss 0.477
Epoch 52 iteration 0060/0185: training loss 0.470
Epoch 52 iteration 0080/0185: training loss 0.466
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Epoch 52 iteration 0140/0186: training loss 0.468
Epoch 52 iteration 0160/0186: training loss 0.466
Epoch 52 iteration 0180/0186: training loss 0.465
Epoch 52 validation pixAcc: 0.940, mIoU: 0.582
Epoch 53 iteration 0020/0185: training loss 0.474
Epoch 53 iteration 0040/0185: training loss 0.471
Epoch 53 iteration 0060/0185: training loss 0.465
Epoch 53 iteration 0080/0185: training loss 0.478
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Epoch 53 iteration 0140/0186: training loss 0.478
Epoch 53 iteration 0160/0186: training loss 0.474
Epoch 53 iteration 0180/0186: training loss 0.477
Epoch 53 validation pixAcc: 0.937, mIoU: 0.586
Epoch 54 iteration 0020/0185: training loss 0.471
Epoch 54 iteration 0040/0185: training loss 0.469
Epoch 54 iteration 0060/0185: training loss 0.466
Epoch 54 iteration 0080/0185: training loss 0.471
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Epoch 54 iteration 0120/0185: training loss 0.473
Epoch 54 iteration 0140/0186: training loss 0.474
Epoch 54 iteration 0160/0186: training loss 0.480
Epoch 54 iteration 0180/0186: training loss 0.486
Epoch 54 validation pixAcc: 0.929, mIoU: 0.547
Epoch 55 iteration 0020/0185: training loss 0.506
Epoch 55 iteration 0040/0185: training loss 0.507
Epoch 55 iteration 0060/0185: training loss 0.507
Epoch 55 iteration 0080/0185: training loss 0.504
Epoch 55 iteration 0100/0185: training loss 0.508
Epoch 55 iteration 0120/0185: training loss 0.523
Epoch 55 iteration 0140/0186: training loss 0.527
Epoch 55 iteration 0160/0186: training loss 0.519
Epoch 55 iteration 0180/0186: training loss 0.519
Epoch 55 validation pixAcc: 0.936, mIoU: 0.563
Epoch 56 iteration 0020/0185: training loss 0.459
Epoch 56 iteration 0040/0185: training loss 0.458
Epoch 56 iteration 0060/0185: training loss 0.465
Epoch 56 iteration 0080/0185: training loss 0.463
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Epoch 56 iteration 0120/0185: training loss 0.460
Epoch 56 iteration 0140/0186: training loss 0.457
Epoch 56 iteration 0160/0186: training loss 0.465
Epoch 56 iteration 0180/0186: training loss 0.467
Epoch 56 validation pixAcc: 0.940, mIoU: 0.582
Epoch 57 iteration 0020/0185: training loss 0.498
Epoch 57 iteration 0040/0185: training loss 0.478
Epoch 57 iteration 0060/0185: training loss 0.488
Epoch 57 iteration 0080/0185: training loss 0.482
Epoch 57 iteration 0100/0185: training loss 0.481
Epoch 57 iteration 0120/0185: training loss 0.479
Epoch 57 iteration 0140/0186: training loss 0.478
Epoch 57 iteration 0160/0186: training loss 0.475
Epoch 57 iteration 0180/0186: training loss 0.473
Epoch 57 validation pixAcc: 0.942, mIoU: 0.598
Epoch 58 iteration 0020/0185: training loss 0.493
Epoch 58 iteration 0040/0185: training loss 0.477
Epoch 58 iteration 0060/0185: training loss 0.469
Epoch 58 iteration 0080/0185: training loss 0.480
Epoch 58 iteration 0100/0185: training loss 0.482
Epoch 58 iteration 0120/0185: training loss 0.491
Epoch 58 iteration 0140/0186: training loss 0.488
Epoch 58 iteration 0160/0186: training loss 0.500
Epoch 58 iteration 0180/0186: training loss 0.498
Epoch 58 validation pixAcc: 0.939, mIoU: 0.574
Epoch 59 iteration 0020/0185: training loss 0.461
Epoch 59 iteration 0040/0185: training loss 0.439
Epoch 59 iteration 0060/0185: training loss 0.447
Epoch 59 iteration 0080/0185: training loss 0.473
Epoch 59 iteration 0100/0185: training loss 0.466
Epoch 59 iteration 0120/0185: training loss 0.467
Epoch 59 iteration 0140/0186: training loss 0.463
Epoch 59 iteration 0160/0186: training loss 0.464
Epoch 59 iteration 0180/0186: training loss 0.463
Epoch 59 validation pixAcc: 0.939, mIoU: 0.587
Epoch 60 iteration 0020/0185: training loss 0.456
Epoch 60 iteration 0040/0185: training loss 0.455
Epoch 60 iteration 0060/0185: training loss 0.443
Epoch 60 iteration 0080/0185: training loss 0.441
Epoch 60 iteration 0100/0185: training loss 0.437
Epoch 60 iteration 0120/0185: training loss 0.444
Epoch 60 iteration 0140/0186: training loss 0.448
Epoch 60 iteration 0160/0186: training loss 0.449
Epoch 60 iteration 0180/0186: training loss 0.448
Epoch 60 validation pixAcc: 0.942, mIoU: 0.601
Epoch 61 iteration 0020/0185: training loss 0.454
Epoch 61 iteration 0040/0185: training loss 0.435
Epoch 61 iteration 0060/0185: training loss 0.440
Epoch 61 iteration 0080/0185: training loss 0.438
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Epoch 61 iteration 0140/0186: training loss 0.440
Epoch 61 iteration 0160/0186: training loss 0.439
Epoch 61 iteration 0180/0186: training loss 0.440
Epoch 61 validation pixAcc: 0.941, mIoU: 0.595
Epoch 62 iteration 0020/0185: training loss 0.410
Epoch 62 iteration 0040/0185: training loss 0.416
Epoch 62 iteration 0060/0185: training loss 0.421
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Epoch 62 iteration 0100/0185: training loss 0.444
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Epoch 62 iteration 0140/0186: training loss 0.447
Epoch 62 iteration 0160/0186: training loss 0.444
Epoch 62 iteration 0180/0186: training loss 0.451
Epoch 62 validation pixAcc: 0.935, mIoU: 0.544
Epoch 63 iteration 0020/0185: training loss 0.494
Epoch 63 iteration 0040/0185: training loss 0.467
Epoch 63 iteration 0060/0185: training loss 0.471
Epoch 63 iteration 0080/0185: training loss 0.462
Epoch 63 iteration 0100/0185: training loss 0.456
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Epoch 63 iteration 0140/0185: training loss 0.451
Epoch 63 iteration 0160/0185: training loss 0.448
Epoch 63 iteration 0180/0185: training loss 0.449
Epoch 63 validation pixAcc: 0.944, mIoU: 0.607
Epoch 64 iteration 0020/0185: training loss 0.431
Epoch 64 iteration 0040/0185: training loss 0.430
Epoch 64 iteration 0060/0185: training loss 0.436
Epoch 64 iteration 0080/0185: training loss 0.441
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Epoch 64 iteration 0120/0185: training loss 0.428
Epoch 64 iteration 0140/0186: training loss 0.428
Epoch 64 iteration 0160/0186: training loss 0.432
Epoch 64 iteration 0180/0186: training loss 0.429
Epoch 64 validation pixAcc: 0.943, mIoU: 0.608
Epoch 65 iteration 0020/0185: training loss 0.439
Epoch 65 iteration 0040/0185: training loss 0.440
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Epoch 65 iteration 0140/0186: training loss 0.435
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Epoch 65 iteration 0180/0186: training loss 0.439
Epoch 65 validation pixAcc: 0.942, mIoU: 0.602
Epoch 66 iteration 0020/0185: training loss 0.434
Epoch 66 iteration 0040/0185: training loss 0.445
Epoch 66 iteration 0060/0185: training loss 0.438
Epoch 66 iteration 0080/0185: training loss 0.430
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Epoch 66 iteration 0120/0185: training loss 0.429
Epoch 66 iteration 0140/0186: training loss 0.427
Epoch 66 iteration 0160/0186: training loss 0.426
Epoch 66 iteration 0180/0186: training loss 0.424
Epoch 66 validation pixAcc: 0.943, mIoU: 0.597
Epoch 67 iteration 0020/0185: training loss 0.482
Epoch 67 iteration 0040/0185: training loss 0.491
Epoch 67 iteration 0060/0185: training loss 0.488
Epoch 67 iteration 0080/0185: training loss 0.479
Epoch 67 iteration 0100/0185: training loss 0.471
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Epoch 67 iteration 0140/0186: training loss 0.469
Epoch 67 iteration 0160/0186: training loss 0.476
Epoch 67 iteration 0180/0186: training loss 0.480
Epoch 67 validation pixAcc: 0.940, mIoU: 0.580
Epoch 68 iteration 0020/0185: training loss 0.461
Epoch 68 iteration 0040/0185: training loss 0.440
Epoch 68 iteration 0060/0185: training loss 0.429
Epoch 68 iteration 0080/0185: training loss 0.460
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Epoch 68 iteration 0120/0185: training loss 0.459
Epoch 68 iteration 0140/0186: training loss 0.455
Epoch 68 iteration 0160/0186: training loss 0.455
Epoch 68 iteration 0180/0186: training loss 0.465
Epoch 68 validation pixAcc: 0.941, mIoU: 0.574
Epoch 69 iteration 0020/0185: training loss 0.403
Epoch 69 iteration 0040/0185: training loss 0.415
Epoch 69 iteration 0060/0185: training loss 0.414
Epoch 69 iteration 0080/0185: training loss 0.433
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Epoch 69 iteration 0140/0186: training loss 0.435
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Epoch 69 validation pixAcc: 0.942, mIoU: 0.597
Epoch 70 iteration 0020/0185: training loss 0.424
Epoch 70 iteration 0040/0185: training loss 0.430
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Epoch 70 iteration 0080/0185: training loss 0.443
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Epoch 70 iteration 0180/0186: training loss 0.449
Epoch 70 validation pixAcc: 0.927, mIoU: 0.551
Epoch 71 iteration 0020/0185: training loss 0.506
Epoch 71 iteration 0040/0185: training loss 0.471
Epoch 71 iteration 0060/0185: training loss 0.465
Epoch 71 iteration 0080/0185: training loss 0.456
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Epoch 71 iteration 0140/0186: training loss 0.454
Epoch 71 iteration 0160/0186: training loss 0.451
Epoch 71 iteration 0180/0186: training loss 0.448
Epoch 71 validation pixAcc: 0.944, mIoU: 0.607
Epoch 72 iteration 0020/0185: training loss 0.427
Epoch 72 iteration 0040/0185: training loss 0.417
Epoch 72 iteration 0060/0185: training loss 0.420
Epoch 72 iteration 0080/0185: training loss 0.434
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Epoch 72 iteration 0140/0186: training loss 0.437
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Epoch 72 iteration 0180/0186: training loss 0.434
Epoch 72 validation pixAcc: 0.945, mIoU: 0.619
Epoch 73 iteration 0020/0185: training loss 0.426
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Epoch 73 iteration 0140/0186: training loss 0.420
Epoch 73 iteration 0160/0186: training loss 0.418
Epoch 73 iteration 0180/0186: training loss 0.415
Epoch 73 validation pixAcc: 0.945, mIoU: 0.626
Epoch 74 iteration 0020/0185: training loss 0.421
Epoch 74 iteration 0040/0185: training loss 0.412
Epoch 74 iteration 0060/0185: training loss 0.409
Epoch 74 iteration 0080/0185: training loss 0.419
Epoch 74 iteration 0100/0185: training loss 0.431
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Epoch 74 iteration 0140/0186: training loss 0.429
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Epoch 74 iteration 0180/0186: training loss 0.436
Epoch 74 validation pixAcc: 0.938, mIoU: 0.572
Epoch 75 iteration 0020/0185: training loss 0.480
Epoch 75 iteration 0040/0185: training loss 0.475
Epoch 75 iteration 0060/0185: training loss 0.469
Epoch 75 iteration 0080/0185: training loss 0.462
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Epoch 75 iteration 0120/0185: training loss 0.453
Epoch 75 iteration 0140/0186: training loss 0.451
Epoch 75 iteration 0160/0186: training loss 0.448
Epoch 75 iteration 0180/0186: training loss 0.442
Epoch 75 validation pixAcc: 0.943, mIoU: 0.615
Epoch 76 iteration 0020/0185: training loss 0.419
Epoch 76 iteration 0040/0185: training loss 0.438
Epoch 76 iteration 0060/0185: training loss 0.424
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Epoch 76 iteration 0140/0186: training loss 0.426
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Epoch 76 iteration 0180/0186: training loss 0.428
Epoch 76 validation pixAcc: 0.941, mIoU: 0.588
Epoch 77 iteration 0020/0185: training loss 0.419
Epoch 77 iteration 0040/0185: training loss 0.445
Epoch 77 iteration 0060/0185: training loss 0.440
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Epoch 77 iteration 0140/0186: training loss 0.440
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Epoch 77 iteration 0180/0186: training loss 0.434
Epoch 77 validation pixAcc: 0.941, mIoU: 0.592
Epoch 78 iteration 0020/0185: training loss 0.406
Epoch 78 iteration 0040/0185: training loss 0.414
Epoch 78 iteration 0060/0185: training loss 0.410
Epoch 78 iteration 0080/0185: training loss 0.424
Epoch 78 iteration 0100/0185: training loss 0.433
Epoch 78 iteration 0120/0185: training loss 0.451
Epoch 78 iteration 0140/0186: training loss 0.449
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Epoch 78 iteration 0180/0186: training loss 0.450
Epoch 78 validation pixAcc: 0.944, mIoU: 0.613
Epoch 79 iteration 0020/0185: training loss 0.424
Epoch 79 iteration 0040/0185: training loss 0.420
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Epoch 79 validation pixAcc: 0.946, mIoU: 0.624
Epoch 80 iteration 0020/0185: training loss 0.400
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Epoch 80 iteration 0140/0186: training loss 0.421
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Epoch 80 iteration 0180/0186: training loss 0.427
Epoch 80 validation pixAcc: 0.942, mIoU: 0.569
Epoch 81 iteration 0020/0185: training loss 0.398
Epoch 81 iteration 0040/0185: training loss 0.389
Epoch 81 iteration 0060/0185: training loss 0.399
Epoch 81 iteration 0080/0185: training loss 0.406
Epoch 81 iteration 0100/0185: training loss 0.413
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Epoch 81 iteration 0140/0186: training loss 0.413
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Epoch 81 iteration 0180/0186: training loss 0.413
Epoch 81 validation pixAcc: 0.946, mIoU: 0.628
Epoch 82 iteration 0020/0185: training loss 0.382
Epoch 82 iteration 0040/0185: training loss 0.369
Epoch 82 iteration 0060/0185: training loss 0.381
Epoch 82 iteration 0080/0185: training loss 0.392
Epoch 82 iteration 0100/0185: training loss 0.391
Epoch 82 iteration 0120/0185: training loss 0.393
Epoch 82 iteration 0140/0186: training loss 0.395
Epoch 82 iteration 0160/0186: training loss 0.400
Epoch 82 iteration 0180/0186: training loss 0.409
Epoch 82 validation pixAcc: 0.944, mIoU: 0.607
Epoch 83 iteration 0020/0185: training loss 0.430
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Epoch 83 iteration 0140/0186: training loss 0.432
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Epoch 83 validation pixAcc: 0.947, mIoU: 0.632
Epoch 84 iteration 0020/0185: training loss 0.431
Epoch 84 iteration 0040/0185: training loss 0.408
Epoch 84 iteration 0060/0185: training loss 0.402
Epoch 84 iteration 0080/0185: training loss 0.399
Epoch 84 iteration 0100/0185: training loss 0.397
Epoch 84 iteration 0120/0185: training loss 0.402
Epoch 84 iteration 0140/0186: training loss 0.405
Epoch 84 iteration 0160/0186: training loss 0.400
Epoch 84 iteration 0180/0186: training loss 0.401
Epoch 84 validation pixAcc: 0.946, mIoU: 0.626
Epoch 85 iteration 0020/0185: training loss 0.404
Epoch 85 iteration 0040/0185: training loss 0.460
Epoch 85 iteration 0060/0185: training loss 0.457
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Epoch 85 iteration 0140/0186: training loss 0.443
Epoch 85 iteration 0160/0186: training loss 0.436
Epoch 85 iteration 0180/0186: training loss 0.433
Epoch 85 validation pixAcc: 0.946, mIoU: 0.616
Epoch 86 iteration 0020/0185: training loss 0.394
Epoch 86 iteration 0040/0185: training loss 0.415
Epoch 86 iteration 0060/0185: training loss 0.419
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Epoch 86 validation pixAcc: 0.946, mIoU: 0.629
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Epoch 87 validation pixAcc: 0.947, mIoU: 0.629
Epoch 88 iteration 0020/0185: training loss 0.423
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Epoch 88 validation pixAcc: 0.946, mIoU: 0.634
Epoch 89 iteration 0020/0185: training loss 0.457
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Epoch 89 validation pixAcc: 0.947, mIoU: 0.633
Epoch 90 iteration 0020/0185: training loss 0.389
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Epoch 90 validation pixAcc: 0.946, mIoU: 0.626
Epoch 91 iteration 0020/0185: training loss 0.409
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Epoch 91 validation pixAcc: 0.947, mIoU: 0.632
Epoch 92 iteration 0020/0185: training loss 0.387
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Epoch 92 validation pixAcc: 0.948, mIoU: 0.631
Epoch 93 iteration 0020/0185: training loss 0.381
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Epoch 93 validation pixAcc: 0.947, mIoU: 0.628
Epoch 94 iteration 0020/0185: training loss 0.383
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Epoch 94 validation pixAcc: 0.946, mIoU: 0.614
Epoch 95 iteration 0020/0185: training loss 0.445
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Epoch 95 validation pixAcc: 0.947, mIoU: 0.645
Epoch 96 iteration 0020/0185: training loss 0.361
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Epoch 96 validation pixAcc: 0.947, mIoU: 0.626
Epoch 97 iteration 0020/0185: training loss 0.369
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Epoch 97 validation pixAcc: 0.947, mIoU: 0.628
Epoch 98 iteration 0020/0185: training loss 0.397
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Epoch 98 validation pixAcc: 0.915, mIoU: 0.490
Epoch 99 iteration 0020/0185: training loss 0.517
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Epoch 99 iteration 0060/0185: training loss 0.467
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Epoch 99 validation pixAcc: 0.945, mIoU: 0.632
Epoch 100 iteration 0020/0185: training loss 0.400
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Epoch 100 validation pixAcc: 0.941, mIoU: 0.594
Epoch 101 iteration 0020/0185: training loss 0.436
Epoch 101 iteration 0040/0185: training loss 0.417
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Epoch 101 validation pixAcc: 0.945, mIoU: 0.610
Epoch 102 iteration 0020/0185: training loss 0.414
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Epoch 102 validation pixAcc: 0.945, mIoU: 0.624
Epoch 103 iteration 0020/0185: training loss 0.399
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Epoch 103 validation pixAcc: 0.946, mIoU: 0.629
Epoch 104 iteration 0020/0185: training loss 0.382
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Epoch 104 validation pixAcc: 0.945, mIoU: 0.632
Epoch 105 iteration 0020/0185: training loss 0.366
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Epoch 105 validation pixAcc: 0.948, mIoU: 0.628
Epoch 106 iteration 0020/0185: training loss 0.380
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Epoch 106 validation pixAcc: 0.944, mIoU: 0.634
Epoch 107 iteration 0020/0185: training loss 0.382
Epoch 107 iteration 0040/0185: training loss 0.375
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Epoch 107 validation pixAcc: 0.949, mIoU: 0.655
Epoch 108 iteration 0020/0185: training loss 0.359
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Epoch 108 validation pixAcc: 0.948, mIoU: 0.650
Epoch 109 iteration 0020/0185: training loss 0.374
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Epoch 109 iteration 0140/0186: training loss 0.397
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Epoch 109 validation pixAcc: 0.946, mIoU: 0.626
Epoch 110 iteration 0020/0185: training loss 0.361
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Epoch 110 validation pixAcc: 0.943, mIoU: 0.613
Epoch 111 iteration 0020/0185: training loss 0.385
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Epoch 111 validation pixAcc: 0.948, mIoU: 0.647
Epoch 112 iteration 0020/0185: training loss 0.387
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Epoch 112 validation pixAcc: 0.947, mIoU: 0.622
Epoch 113 iteration 0020/0185: training loss 0.388
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Epoch 113 validation pixAcc: 0.946, mIoU: 0.627
Epoch 114 iteration 0020/0185: training loss 0.379
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Epoch 114 validation pixAcc: 0.948, mIoU: 0.635
Epoch 115 iteration 0020/0185: training loss 0.342
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Epoch 115 validation pixAcc: 0.950, mIoU: 0.656
Epoch 116 iteration 0020/0185: training loss 0.367
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Epoch 116 validation pixAcc: 0.948, mIoU: 0.646
Epoch 117 iteration 0020/0185: training loss 0.352
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Epoch 117 validation pixAcc: 0.949, mIoU: 0.644
Epoch 118 iteration 0020/0185: training loss 0.361
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Epoch 118 validation pixAcc: 0.949, mIoU: 0.634
Epoch 119 iteration 0020/0185: training loss 0.366
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Epoch 119 validation pixAcc: 0.949, mIoU: 0.642
Epoch 120 iteration 0020/0185: training loss 0.391
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Epoch 120 validation pixAcc: 0.950, mIoU: 0.641
Epoch 121 iteration 0020/0185: training loss 0.379
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Epoch 121 validation pixAcc: 0.948, mIoU: 0.639
Epoch 122 iteration 0020/0185: training loss 0.360
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Epoch 122 validation pixAcc: 0.950, mIoU: 0.648
Epoch 123 iteration 0020/0185: training loss 0.360
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Epoch 123 validation pixAcc: 0.950, mIoU: 0.647
Epoch 124 iteration 0020/0185: training loss 0.361
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Epoch 124 validation pixAcc: 0.951, mIoU: 0.650
Epoch 125 iteration 0020/0185: training loss 0.373
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Epoch 125 validation pixAcc: 0.948, mIoU: 0.643
Epoch 126 iteration 0020/0185: training loss 0.391
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Epoch 126 validation pixAcc: 0.949, mIoU: 0.650
Epoch 127 iteration 0020/0185: training loss 0.339
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Epoch 127 validation pixAcc: 0.949, mIoU: 0.640
Epoch 128 iteration 0020/0185: training loss 0.336
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Epoch 128 validation pixAcc: 0.949, mIoU: 0.643
Epoch 129 iteration 0020/0185: training loss 0.349
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Epoch 129 validation pixAcc: 0.949, mIoU: 0.654
Epoch 130 iteration 0020/0185: training loss 0.338
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Epoch 130 validation pixAcc: 0.949, mIoU: 0.639
Epoch 131 iteration 0020/0185: training loss 0.334
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Epoch 131 validation pixAcc: 0.947, mIoU: 0.634
Epoch 132 iteration 0020/0185: training loss 0.359
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Epoch 132 validation pixAcc: 0.950, mIoU: 0.644
Epoch 133 iteration 0020/0185: training loss 0.367
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Epoch 133 validation pixAcc: 0.948, mIoU: 0.643
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Epoch 134 validation pixAcc: 0.949, mIoU: 0.643
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Epoch 135 validation pixAcc: 0.949, mIoU: 0.645
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Epoch 136 validation pixAcc: 0.950, mIoU: 0.644
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Epoch 137 validation pixAcc: 0.950, mIoU: 0.644
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Epoch 138 validation pixAcc: 0.951, mIoU: 0.654
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Epoch 139 validation pixAcc: 0.951, mIoU: 0.655
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Epoch 140 validation pixAcc: 0.948, mIoU: 0.635
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Epoch 141 validation pixAcc: 0.950, mIoU: 0.654
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Epoch 142 validation pixAcc: 0.949, mIoU: 0.644
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Epoch 143 validation pixAcc: 0.948, mIoU: 0.656
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Epoch 144 validation pixAcc: 0.952, mIoU: 0.666
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Epoch 145 validation pixAcc: 0.949, mIoU: 0.647
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Epoch 146 validation pixAcc: 0.949, mIoU: 0.647
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Epoch 147 validation pixAcc: 0.951, mIoU: 0.654
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Epoch 148 validation pixAcc: 0.952, mIoU: 0.666
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Epoch 149 validation pixAcc: 0.950, mIoU: 0.664
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Epoch 150 validation pixAcc: 0.951, mIoU: 0.663
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Epoch 151 validation pixAcc: 0.952, mIoU: 0.670
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Epoch 152 validation pixAcc: 0.951, mIoU: 0.661
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Epoch 156 validation pixAcc: 0.951, mIoU: 0.659
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Epoch 158 validation pixAcc: 0.951, mIoU: 0.669
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Epoch 159 validation pixAcc: 0.951, mIoU: 0.651
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Epoch 160 validation pixAcc: 0.949, mIoU: 0.650
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Epoch 163 validation pixAcc: 0.953, mIoU: 0.659
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Epoch 164 validation pixAcc: 0.952, mIoU: 0.652
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Epoch 165 validation pixAcc: 0.951, mIoU: 0.648
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Epoch 166 validation pixAcc: 0.952, mIoU: 0.657
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Epoch 167 validation pixAcc: 0.952, mIoU: 0.662
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Epoch 168 validation pixAcc: 0.949, mIoU: 0.647
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Epoch 169 validation pixAcc: 0.952, mIoU: 0.663
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Epoch 171 validation pixAcc: 0.942, mIoU: 0.654
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Epoch 172 validation pixAcc: 0.952, mIoU: 0.664
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Epoch 173 validation pixAcc: 0.953, mIoU: 0.673
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Epoch 175 validation pixAcc: 0.953, mIoU: 0.678
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Epoch 176 validation pixAcc: 0.953, mIoU: 0.667
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Epoch 177 validation pixAcc: 0.953, mIoU: 0.672
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Epoch 178 validation pixAcc: 0.953, mIoU: 0.671
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Epoch 179 validation pixAcc: 0.953, mIoU: 0.670
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Epoch 189 validation pixAcc: 0.953, mIoU: 0.669
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Epoch 190 validation pixAcc: 0.953, mIoU: 0.669
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Epoch 191 validation pixAcc: 0.953, mIoU: 0.664
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Epoch 192 validation pixAcc: 0.953, mIoU: 0.663
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Epoch 227 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 228 validation pixAcc: 0.955, mIoU: 0.679
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Epoch 229 validation pixAcc: 0.955, mIoU: 0.679
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Epoch 230 validation pixAcc: 0.955, mIoU: 0.676
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Epoch 231 validation pixAcc: 0.955, mIoU: 0.675
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Epoch 232 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 233 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 234 validation pixAcc: 0.955, mIoU: 0.678
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Epoch 235 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 236 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 237 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 238 validation pixAcc: 0.955, mIoU: 0.681
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Epoch 239 validation pixAcc: 0.955, mIoU: 0.682