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diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py
new file mode 100644
index 0000000000..4e6f44bdf5
--- /dev/null
+++ b/configs/_base_/datasets/deepGlobe.py
@@ -0,0 +1,69 @@
+#configs/_base_/datasets/deepGlobe.py
+#mmseseg/configs/_base_/datasets/deepGlobe.py
+# dataset settings
+dataset_type = 'DeepGlobeDataset'
+data_root = 'data/deepglobe_ds/'
+crop_size = (256, 256)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(512, 512),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(512, 512), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[
+            [
+                dict(type='Resize', scale_factor=r, keep_ratio=True)
+                for r in img_ratios
+            ],
+            [
+                dict(type='RandomFlip', prob=0., direction='horizontal'),
+                dict(type='RandomFlip', prob=1., direction='horizontal')
+            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+        ])
+]
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator
diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py
index 272b4d2467..96aa87149a 100644
--- a/configs/_base_/default_runtime.py
+++ b/configs/_base_/default_runtime.py
@@ -4,7 +4,9 @@
     mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
     dist_cfg=dict(backend='nccl'),
 )
-vis_backends = [dict(type='LocalVisBackend')]
+vis_backends = [dict(type='LocalVisBackend'),
+                dict(type='TensorboardVisBackend')]
+
 visualizer = dict(
     type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
 log_processor = dict(by_epoch=False)
diff --git a/configs/_base_/models/ccnet_r50-d8.py b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py
similarity index 92%
rename from configs/_base_/models/ccnet_r50-d8.py
rename to configs/_base_/models/ccnet_r50-d8_deepglobe_5.py
index 575d8eb459..e8909c99a6 100644
--- a/configs/_base_/models/ccnet_r50-d8.py
+++ b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py
@@ -2,8 +2,8 @@
 norm_cfg = dict(type='SyncBN', requires_grad=True)
 data_preprocessor = dict(
     type='SegDataPreProcessor',
-    mean=[123.675, 116.28, 103.53],
-    std=[58.395, 57.12, 57.375],
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[0.1451, 0.1116, 0.1013],
     bgr_to_rgb=True,
     pad_val=0,
     seg_pad_val=255)
@@ -29,7 +29,7 @@
         channels=512,
         recurrence=2,
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
@@ -42,7 +42,7 @@
         num_convs=1,
         concat_input=False,
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
diff --git a/configs/_base_/models/deeplabv3_r50-d8.py b/configs/_base_/models/deeplabv3_r50-d8.py
index 22efe9a6ca..caa74a096b 100644
--- a/configs/_base_/models/deeplabv3_r50-d8.py
+++ b/configs/_base_/models/deeplabv3_r50-d8.py
@@ -1,9 +1,10 @@
+#configs/_base_/models/deeplabv3_r50-d8.py
 # model settings
 norm_cfg = dict(type='SyncBN', requires_grad=True)
 data_preprocessor = dict(
     type='SegDataPreProcessor',
-    mean=[123.675, 116.28, 103.53],
-    std=[58.395, 57.12, 57.375],
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[ 0.1451, 0.1116, 0.1013],
     bgr_to_rgb=True,
     pad_val=0,
     seg_pad_val=255)
@@ -29,7 +30,7 @@
         channels=512,
         dilations=(1, 12, 24, 36),
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
@@ -42,7 +43,7 @@
         num_convs=1,
         concat_input=False,
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
diff --git a/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py
new file mode 100644
index 0000000000..2451ce45a3
--- /dev/null
+++ b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py
@@ -0,0 +1,54 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[ 0.1451, 0.1116, 0.1013],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='DepthwiseSeparableASPPHead',
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        dilations=(1, 12, 24, 36),
+        c1_in_channels=256,
+        c1_channels=48,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
diff --git a/configs/_base_/models/fcn_hr18_deepGlobe.py b/configs/_base_/models/fcn_hr18_deepGlobe.py
new file mode 100644
index 0000000000..0faaa1e567
--- /dev/null
+++ b/configs/_base_/models/fcn_hr18_deepGlobe.py
@@ -0,0 +1,60 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[ 0.1351, 0.1022, 0.0931],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    backbone=dict(
+        type='HRNet',
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(18, 36)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(18, 36, 72)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(18, 36, 72, 144)))),
+    decode_head=dict(
+        type='FCNHead',
+        in_channels=[18, 36, 72, 144],
+        in_index=(0, 1, 2, 3),
+        channels=sum([18, 36, 72, 144]),
+        input_transform='resize_concat',
+        kernel_size=1,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=-1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
diff --git a/configs/_base_/models/fcn_r50-d8deepglobe.py b/configs/_base_/models/fcn_r50-d8deepglobe.py
new file mode 100644
index 0000000000..3e033fe47d
--- /dev/null
+++ b/configs/_base_/models/fcn_r50-d8deepglobe.py
@@ -0,0 +1,53 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[0.1351, 0.1022, 0.0931],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='FCNHead',
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        num_convs=2,
+        concat_input=True,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
diff --git a/configs/_base_/models/gcnet_r50-d8_deepGlobe.py b/configs/_base_/models/gcnet_r50-d8_deepGlobe.py
new file mode 100644
index 0000000000..e7e509f5e4
--- /dev/null
+++ b/configs/_base_/models/gcnet_r50-d8_deepGlobe.py
@@ -0,0 +1,54 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[ 0.1451, 0.1116, 0.1013],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='GCHead',
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        ratio=1 / 4.,
+        pooling_type='att',
+        fusion_types=('channel_add', ),
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
diff --git a/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py
new file mode 100644
index 0000000000..8cefb930fe
--- /dev/null
+++ b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py
@@ -0,0 +1,58 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[0.4082, 0.3791, 0.2815],
+    std=[0.1351, 0.1022, 0.0931],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained=None,
+    backbone=dict(
+        type='UNet',
+        in_channels=3,
+        base_channels=64,
+        num_stages=5,
+        strides=(1, 1, 1, 1, 1),
+        enc_num_convs=(2, 2, 2, 2, 2),
+        dec_num_convs=(2, 2, 2, 2),
+        downsamples=(True, True, True, True),
+        enc_dilations=(1, 1, 1, 1, 1),
+        dec_dilations=(1, 1, 1, 1),
+        with_cp=False,
+        conv_cfg=None,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='ReLU'),
+        upsample_cfg=dict(type='InterpConv'),
+        norm_eval=False),
+    decode_head=dict(
+        type='PSPHead',
+        in_channels=64,
+        in_index=4,
+        channels=16,
+        pool_scales=(1, 2, 3, 6),
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=128,
+        in_index=3,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=7,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='slide', crop_size=256, stride=170))
diff --git a/configs/_base_/schedules/schedule_40k.py b/configs/_base_/schedules/schedule_40k.py
index 4b823339a2..5512eb4e60 100644
--- a/configs/_base_/schedules/schedule_40k.py
+++ b/configs/_base_/schedules/schedule_40k.py
@@ -8,11 +8,11 @@
         eta_min=1e-4,
         power=0.9,
         begin=0,
-        end=40000,
+        end=10000,
         by_epoch=False)
 ]
 # training schedule for 40k
-train_cfg = dict(type='IterBasedTrainLoop', max_iters=40000, val_interval=4000)
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
 default_hooks = dict(
diff --git a/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py
new file mode 100644
index 0000000000..55ee6208d0
--- /dev/null
+++ b/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py
@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/ccnet_r50-d8_deepglobe_5.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py
new file mode 100644
index 0000000000..ed3da73684
--- /dev/null
+++ b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py
@@ -0,0 +1,8 @@
+#configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8_deepGlobe.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py
new file mode 100644
index 0000000000..bbd63a3be1
--- /dev/null
+++ b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py
@@ -0,0 +1,8 @@
+_base_ = [
+    '../_base_/models/deeplabv3plus_r50-d8_deepglobe.py',
+    '../_base_/datasets/deepglobe.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py b/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py
new file mode 100644
index 0000000000..23f642087f
--- /dev/null
+++ b/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py
@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/fcn_r50-d8deepglobe.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py b/configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py
similarity index 64%
rename from configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
rename to configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py
index 84fc51a6b3..c359f534ba 100644
--- a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+++ b/configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py
@@ -1,7 +1,7 @@
 _base_ = [
-    '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/deepGlobe.py',
     '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
 ]
-crop_size = (512, 1024)
+crop_size = (256, 256)
 data_preprocessor = dict(size=crop_size)
 model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py b/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py
new file mode 100644
index 0000000000..e5f8eecd63
--- /dev/null
+++ b/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py
@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/fcn_hr18_deepGlobe.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py
new file mode 100644
index 0000000000..a37534cd09
--- /dev/null
+++ b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py
@@ -0,0 +1,9 @@
+_base_ = [
+    '../_base_/models/pspnet_unet_deepglobe_s5-d16.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (256, 256)
+data_preprocessor = dict(size=crop_size)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    test_cfg=dict(crop_size=(256, 256), stride=(85, 85)))
diff --git a/docs/en/stat.py b/docs/en/stat.py
old mode 100755
new mode 100644
diff --git a/docs/zh_cn/stat.py b/docs/zh_cn/stat.py
old mode 100755
new mode 100644
diff --git a/mmseg/configs/_base_/schedules/schedule_40k.py b/mmseg/configs/_base_/schedules/schedule_40k.py
index b4b2ea42b5..f007a5d280 100644
--- a/mmseg/configs/_base_/schedules/schedule_40k.py
+++ b/mmseg/configs/_base_/schedules/schedule_40k.py
@@ -1,34 +1,25 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
-                            LoggerHook, ParamSchedulerHook)
-from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
-from mmengine.optim.scheduler.lr_scheduler import PolyLR
-from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
-from torch.optim.sgd import SGD
-
-from mmseg.engine import SegVisualizationHook
-
+#configs/_base_/schedules/schedule_40k.py
 # optimizer
-optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005)
-optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
-
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None)
+# learning policy
 param_scheduler = [
     dict(
-        type=PolyLR,
+        type='PolyLR',
         eta_min=1e-4,
         power=0.9,
         begin=0,
-        end=40000,
+        end=10000,
         by_epoch=False)
 ]
 # training schedule for 40k
-train_cfg = dict(type=IterBasedTrainLoop, max_iters=40000, val_interval=4000)
-val_cfg = dict(type=ValLoop)
-test_cfg = dict(type=TestLoop)
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 default_hooks = dict(
-    timer=dict(type=IterTimerHook),
-    logger=dict(type=LoggerHook, interval=50, log_metric_by_epoch=False),
-    param_scheduler=dict(type=ParamSchedulerHook),
-    checkpoint=dict(type=CheckpointHook, by_epoch=False, interval=4000),
-    sampler_seed=dict(type=DistSamplerSeedHook),
-    visualization=dict(type=SegVisualizationHook))
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='SegVisualizationHook'))
diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py
index f8ad750d76..907bbf8cef 100644
--- a/mmseg/datasets/__init__.py
+++ b/mmseg/datasets/__init__.py
@@ -1,3 +1,4 @@
+#mmseg/datasets/__init__.py
 # Copyright (c) OpenMMLab. All rights reserved.
 # yapf: disable
 from .ade import ADE20KDataset
@@ -26,6 +27,7 @@
 from .refuge import REFUGEDataset
 from .stare import STAREDataset
 from .synapse import SynapseDataset
+from .deepGlobe import DeepGlobeDataset
 # yapf: disable
 from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad,
                          BioMedical3DRandomCrop, BioMedical3DRandomFlip,
@@ -61,5 +63,5 @@
     'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset',
     'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile',
     'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset',
-    'NYUDataset', 'HSIDrive20Dataset'
+    'NYUDataset', 'HSIDrive20Dataset', 'DeepGlobeDataset'
 ]
diff --git a/mmseg/datasets/deepGlobe.py b/mmseg/datasets/deepGlobe.py
new file mode 100644
index 0000000000..e09004254e
--- /dev/null
+++ b/mmseg/datasets/deepGlobe.py
@@ -0,0 +1,48 @@
+#mmseg/datasets/deepGlobe.py
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmseg.registry import DATASETS
+from .basesegdataset import BaseSegDataset
+import json
+import os
+from datetime import datetime
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import torch
+import torch.utils.data as tdata
+
+@DATASETS.register_module()
+class DeepGlobeDataset(BaseSegDataset):
+    """Deep Globe Dataset.
+
+    The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
+    fixed to '_t.png' for Cityscapes dataset.
+    """
+    METAINFO = dict(
+        classes=('Urban', 'Agriculture', 'Range', 'Forest', 'Water', 'Barren',
+                 'Unknown'),
+        palette=[[0,255,255], [255,255,0], [255,0,255], [0,255,0],
+                 [0,0,255], [255,255,255], [1,1,1]
+                 ])
+    
+    class_dict={
+                "1": "Urban",
+                "2": "Agriculture",
+                "3": "Range",
+                "4": "Forest",
+                "5": "Water",
+                "6": "Barren",
+                "7": "Unknown"
+                }
+    color_map = [
+            [0,255,255], [255,255,0], [255,0,255], [0,255,0],
+            [0,0,255], [255,255,255], [1,1,1]
+            ]
+
+    def __init__(self,
+                 img_suffix='_sat.jpg',
+                 seg_map_suffix='_mask.png',
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
diff --git a/mmseg/utils/__init__.py b/mmseg/utils/__init__.py
index 0a2af58c6e..c74f610500 100644
--- a/mmseg/utils/__init__.py
+++ b/mmseg/utils/__init__.py
@@ -2,7 +2,7 @@
 # yapf: disable
 from .class_names import (ade_classes, ade_palette, bdd100k_classes,
                           bdd100k_palette, cityscapes_classes,
-                          cityscapes_palette, cocostuff_classes,
+                          cityscapes_palette, deepGlobe_classes, deepGlobe_palette, cocostuff_classes,
                           cocostuff_palette, dataset_aliases, get_classes,
                           get_palette, isaid_classes, isaid_palette,
                           loveda_classes, loveda_palette, potsdam_classes,
diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py
index 644e955966..f2c88a560a 100644
--- a/mmseg/utils/class_names.py
+++ b/mmseg/utils/class_names.py
@@ -463,6 +463,12 @@ def bdd100k_classes():
         'bicycle'
     ]
 
+def deepGlobe_classes():
+    return[
+        'Urban', 'Agriculture', 'Range', 'Forest', 'Water', 'Barren',
+        'Unknown'
+    ]
+
 
 def bdd100k_palette():
     """bdd100k palette for external use(same with cityscapes)"""
@@ -487,9 +493,15 @@ def hsidrive_palette():
             [0, 0, 255], [102, 51, 0], [255, 255, 0], [0, 207, 250],
             [255, 166, 0], [0, 204, 204]]
 
+def deepGlobe_palette():
+    """DeepGlobe palette for external use."""
+    return [[0,255,255], [255,255,0], [255,0,255], [0,255,0],
+            [0,0,255], [255,255,255], [1,1,1]]
+
 
 dataset_aliases = {
     'cityscapes': ['cityscapes'],
+    'deepGlobe': ['deepGlobe'],
     'ade': ['ade', 'ade20k'],
     'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'],
     'pcontext': ['pcontext', 'pascal_context', 'voc2010'],
diff --git a/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py b/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py
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diff --git a/projects/medical/2d_image/histopathology/breast_cancer_cell_seg/tools/prepare_dataset.py b/projects/medical/2d_image/histopathology/breast_cancer_cell_seg/tools/prepare_dataset.py
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diff --git a/projects/medical/2d_image/microscopy_images/bactteria_detection/tools/prepare_dataset.py b/projects/medical/2d_image/microscopy_images/bactteria_detection/tools/prepare_dataset.py
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diff --git a/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py b/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py
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diff --git a/tests/data/dsdl_seg/defs/segmentation-def.yaml b/tests/data/dsdl_seg/defs/segmentation-def.yaml
old mode 100755
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diff --git a/tests/data/dsdl_seg/set-train/train.yaml b/tests/data/dsdl_seg/set-train/train.yaml
old mode 100755
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diff --git a/tests/data/dsdl_seg/set-train/train_samples.json b/tests/data/dsdl_seg/set-train/train_samples.json
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diff --git a/tools/dist_test.sh b/tools/dist_test.sh
old mode 100755
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diff --git a/tools/dist_train.sh b/tools/dist_train.sh
old mode 100755
new mode 100644
diff --git a/tools/model_test.py b/tools/model_test.py
new file mode 100644
index 0000000000..7263db435c
--- /dev/null
+++ b/tools/model_test.py
@@ -0,0 +1,70 @@
+import os
+import mmcv
+import numpy as np
+import cv2
+from mmseg.apis import init_model, inference_model
+
+# Configuration and checkpoint paths
+CONFIG_FILE = '/Users/anyego/Desktop/mmsegmentationEQ2/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py'
+CHECKPOINT_FILE = '/Users/anyego/Desktop/mmsegmentationEQ2/workdir/fcn/iter_10000.pth'
+DEVICE = 'cpu'
+
+# Custom color palette
+PALETTE = np.array([
+    [0, 255, 255], [255, 255, 0], [255, 0, 255], 
+    [0, 255, 0], [0, 0, 255], [255, 255, 255], 
+    [0, 0, 0]
+], dtype=np.uint8)
+
+# Directories
+IMAGE_DIR = '/Users/anyego/Desktop/mmsegmentationEQ2/Test_model'
+OUTPUT_DIR = '/Users/anyego/Desktop/mmsegmentationEQ2/PrediccionesFCN'
+
+def initialize_model(config_path, checkpoint_path, device):
+    """Initialize the segmentation model."""
+    return init_model(config_path, checkpoint_path, device=device)
+
+def create_output_directory(directory):
+    """Create the output directory if it doesn't exist."""
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+def get_image_paths(directory):
+    """Retrieve all image paths from the directory."""
+    return [os.path.join(directory, img_name) for img_name in os.listdir(directory)]
+
+def postprocess_mask(mask):
+    """Post-process the predicted mask."""
+    # Apply morphological operations to refine the mask
+    kernel = np.ones((3, 3), np.uint8)
+    refined_mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    return refined_mask
+
+def process_image(model, img_path):
+    """Run inference on a single image and return the color mask."""
+    result = inference_model(model, img_path)
+    mask = result.pred_sem_seg.data.cpu().numpy().astype(np.uint8).squeeze(0)
+    mask = postprocess_mask(mask)
+    return PALETTE[mask]
+
+def save_color_mask(color_mask, output_path):
+    """Save the color mask to the specified path."""
+    mmcv.imwrite(color_mask, output_path)
+
+def main():
+    """Main function to run the segmentation inference."""
+    model = initialize_model(CONFIG_FILE, CHECKPOINT_FILE, DEVICE)
+    create_output_directory(OUTPUT_DIR)
+    
+    for img_path in get_image_paths(IMAGE_DIR):
+        color_mask = process_image(model, img_path)
+        if color_mask.size > 0:
+            output_path = os.path.join(OUTPUT_DIR, os.path.basename(img_path))
+            save_color_mask(color_mask, output_path)
+        else:
+            print(f"Skipping {img_path} due to invalid mask")
+
+    print(f"Inference completed. Results saved to: {OUTPUT_DIR}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
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