diff --git a/mmseg_logs/chase_db1/20240614_171848/vis_data/20240614_171848.json b/mmseg_logs/chase_db1/20240614_171848/vis_data/20240614_171848.json
new file mode 100644
index 0000000000..60401d76f0
--- /dev/null
+++ b/mmseg_logs/chase_db1/20240614_171848/vis_data/20240614_171848.json
@@ -0,0 +1,3 @@
+{"lr": 0.009989084308262066, "data_time": 0.0038670063018798827, "loss": 0.6592155277729035, "decode.loss_ce": 0.47149578332901, "decode.acc_seg": 89.7125244140625, "aux.loss_ce": 0.18771974593400956, "aux.acc_seg": 89.7125244140625, "time": 0.39728693962097167, "iter": 50, "memory": 1717, "step": 50}
+{"lr": 0.00997794446709763, "data_time": 0.003805875778198242, "loss": 0.6025463283061981, "decode.loss_ce": 0.4323257476091385, "decode.acc_seg": 93.8629150390625, "aux.loss_ce": 0.17022058442234994, "aux.acc_seg": 93.8629150390625, "time": 0.3980520725250244, "iter": 100, "memory": 784, "step": 100}
+{"lr": 0.009966803229875268, "data_time": 0.0038983821868896484, "loss": 0.6277212738990784, "decode.loss_ce": 0.448396360874176, "decode.acc_seg": 76.31072998046875, "aux.loss_ce": 0.17932491898536682, "aux.acc_seg": 75.860595703125, "time": 0.3982886791229248, "iter": 150, "memory": 784, "step": 150}
diff --git a/mmseg_logs/chase_db1/20240614_171848/vis_data/config.py b/mmseg_logs/chase_db1/20240614_171848/vis_data/config.py
new file mode 100644
index 0000000000..c064330b97
--- /dev/null
+++ b/mmseg_logs/chase_db1/20240614_171848/vis_data/config.py
@@ -0,0 +1,332 @@
+crop_size = (
+    128,
+    128,
+)
+data_preprocessor = dict(
+    bgr_to_rgb=True,
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    pad_val=0,
+    seg_pad_val=255,
+    size=(
+        128,
+        128,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    type='SegDataPreProcessor')
+data_root = 'data/CHASE_DB1'
+dataset_type = 'ChaseDB1Dataset'
+default_hooks = dict(
+    checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'),
+    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='SegVisualizationHook'))
+default_scope = 'mmseg'
+env_cfg = dict(
+    cudnn_benchmark=True,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+img_ratios = [
+    0.5,
+    0.75,
+    1.0,
+    1.25,
+    1.5,
+    1.75,
+]
+img_scale = (
+    960,
+    999,
+)
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=False)
+model = dict(
+    auxiliary_head=dict(
+        align_corners=False,
+        channels=64,
+        concat_input=False,
+        dropout_ratio=0.1,
+        in_channels=128,
+        in_index=3,
+        loss_decode=dict(
+            loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        num_classes=2,
+        num_convs=1,
+        type='FCNHead'),
+    backbone=dict(
+        act_cfg=dict(type='ReLU'),
+        base_channels=64,
+        conv_cfg=None,
+        dec_dilations=(
+            1,
+            1,
+            1,
+            1,
+        ),
+        dec_num_convs=(
+            2,
+            2,
+            2,
+            2,
+        ),
+        downsamples=(
+            True,
+            True,
+            True,
+            True,
+        ),
+        enc_dilations=(
+            1,
+            1,
+            1,
+            1,
+            1,
+        ),
+        enc_num_convs=(
+            2,
+            2,
+            2,
+            2,
+            2,
+        ),
+        in_channels=3,
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        norm_eval=False,
+        num_stages=5,
+        strides=(
+            1,
+            1,
+            1,
+            1,
+            1,
+        ),
+        type='UNet',
+        upsample_cfg=dict(type='InterpConv'),
+        with_cp=False),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_val=0,
+        seg_pad_val=255,
+        size=(
+            128,
+            128,
+        ),
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='SegDataPreProcessor'),
+    decode_head=dict(
+        align_corners=False,
+        channels=64,
+        concat_input=False,
+        dropout_ratio=0.1,
+        in_channels=64,
+        in_index=4,
+        loss_decode=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        num_classes=2,
+        num_convs=1,
+        type='FCNHead'),
+    pretrained=None,
+    test_cfg=dict(crop_size=(
+        128,
+        128,
+    ), mode='slide', stride=(
+        85,
+        85,
+    )),
+    train_cfg=dict(),
+    type='EncoderDecoder')
+norm_cfg = dict(requires_grad=True, type='SyncBN')
+optim_wrapper = dict(
+    clip_grad=None,
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
+    type='OptimWrapper')
+optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=40000,
+        eta_min=0.0001,
+        power=0.9,
+        type='PolyLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        data_root='data/CHASE_DB1',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                960,
+                999,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='ChaseDB1Dataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mDice',
+    ], type='IoUMetric')
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        960,
+        999,
+    ), type='Resize'),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs'),
+]
+train_cfg = dict(max_iters=40000, type='IterBasedTrainLoop', val_interval=4000)
+train_dataloader = dict(
+    batch_size=4,
+    dataset=dict(
+        dataset=dict(
+            data_prefix=dict(
+                img_path='images/training',
+                seg_map_path='annotations/training'),
+            data_root='data/CHASE_DB1',
+            pipeline=[
+                dict(type='LoadImageFromFile'),
+                dict(type='LoadAnnotations'),
+                dict(
+                    keep_ratio=True,
+                    ratio_range=(
+                        0.5,
+                        2.0,
+                    ),
+                    scale=(
+                        960,
+                        999,
+                    ),
+                    type='RandomResize'),
+                dict(
+                    cat_max_ratio=0.75,
+                    crop_size=(
+                        128,
+                        128,
+                    ),
+                    type='RandomCrop'),
+                dict(prob=0.5, type='RandomFlip'),
+                dict(type='PhotoMetricDistortion'),
+                dict(type='PackSegInputs'),
+            ],
+            type='ChaseDB1Dataset'),
+        times=40000,
+        type='RepeatDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='InfiniteSampler'))
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        keep_ratio=True,
+        ratio_range=(
+            0.5,
+            2.0,
+        ),
+        scale=(
+            960,
+            999,
+        ),
+        type='RandomResize'),
+    dict(cat_max_ratio=0.75, crop_size=(
+        128,
+        128,
+    ), type='RandomCrop'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs'),
+]
+tta_model = dict(type='SegTTAModel')
+tta_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(
+        transforms=[
+            [
+                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
+            ],
+            [
+                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
+                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
+            ],
+            [
+                dict(type='LoadAnnotations'),
+            ],
+            [
+                dict(type='PackSegInputs'),
+            ],
+        ],
+        type='TestTimeAug'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        data_root='data/CHASE_DB1',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                960,
+                999,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='ChaseDB1Dataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mDice',
+    ], type='IoUMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = 'mmseg_logs/chase_db1/'
diff --git a/mmseg_logs/chase_db1/20240614_171848/vis_data/scalars.json b/mmseg_logs/chase_db1/20240614_171848/vis_data/scalars.json
new file mode 100644
index 0000000000..60401d76f0
--- /dev/null
+++ b/mmseg_logs/chase_db1/20240614_171848/vis_data/scalars.json
@@ -0,0 +1,3 @@
+{"lr": 0.009989084308262066, "data_time": 0.0038670063018798827, "loss": 0.6592155277729035, "decode.loss_ce": 0.47149578332901, "decode.acc_seg": 89.7125244140625, "aux.loss_ce": 0.18771974593400956, "aux.acc_seg": 89.7125244140625, "time": 0.39728693962097167, "iter": 50, "memory": 1717, "step": 50}
+{"lr": 0.00997794446709763, "data_time": 0.003805875778198242, "loss": 0.6025463283061981, "decode.loss_ce": 0.4323257476091385, "decode.acc_seg": 93.8629150390625, "aux.loss_ce": 0.17022058442234994, "aux.acc_seg": 93.8629150390625, "time": 0.3980520725250244, "iter": 100, "memory": 784, "step": 100}
+{"lr": 0.009966803229875268, "data_time": 0.0038983821868896484, "loss": 0.6277212738990784, "decode.loss_ce": 0.448396360874176, "decode.acc_seg": 76.31072998046875, "aux.loss_ce": 0.17932491898536682, "aux.acc_seg": 75.860595703125, "time": 0.3982886791229248, "iter": 150, "memory": 784, "step": 150}
diff --git a/mmseg_logs/chase_db1/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py b/mmseg_logs/chase_db1/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
new file mode 100644
index 0000000000..c064330b97
--- /dev/null
+++ b/mmseg_logs/chase_db1/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
@@ -0,0 +1,332 @@
+crop_size = (
+    128,
+    128,
+)
+data_preprocessor = dict(
+    bgr_to_rgb=True,
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    pad_val=0,
+    seg_pad_val=255,
+    size=(
+        128,
+        128,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    type='SegDataPreProcessor')
+data_root = 'data/CHASE_DB1'
+dataset_type = 'ChaseDB1Dataset'
+default_hooks = dict(
+    checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'),
+    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='SegVisualizationHook'))
+default_scope = 'mmseg'
+env_cfg = dict(
+    cudnn_benchmark=True,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+img_ratios = [
+    0.5,
+    0.75,
+    1.0,
+    1.25,
+    1.5,
+    1.75,
+]
+img_scale = (
+    960,
+    999,
+)
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=False)
+model = dict(
+    auxiliary_head=dict(
+        align_corners=False,
+        channels=64,
+        concat_input=False,
+        dropout_ratio=0.1,
+        in_channels=128,
+        in_index=3,
+        loss_decode=dict(
+            loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        num_classes=2,
+        num_convs=1,
+        type='FCNHead'),
+    backbone=dict(
+        act_cfg=dict(type='ReLU'),
+        base_channels=64,
+        conv_cfg=None,
+        dec_dilations=(
+            1,
+            1,
+            1,
+            1,
+        ),
+        dec_num_convs=(
+            2,
+            2,
+            2,
+            2,
+        ),
+        downsamples=(
+            True,
+            True,
+            True,
+            True,
+        ),
+        enc_dilations=(
+            1,
+            1,
+            1,
+            1,
+            1,
+        ),
+        enc_num_convs=(
+            2,
+            2,
+            2,
+            2,
+            2,
+        ),
+        in_channels=3,
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        norm_eval=False,
+        num_stages=5,
+        strides=(
+            1,
+            1,
+            1,
+            1,
+            1,
+        ),
+        type='UNet',
+        upsample_cfg=dict(type='InterpConv'),
+        with_cp=False),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_val=0,
+        seg_pad_val=255,
+        size=(
+            128,
+            128,
+        ),
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='SegDataPreProcessor'),
+    decode_head=dict(
+        align_corners=False,
+        channels=64,
+        concat_input=False,
+        dropout_ratio=0.1,
+        in_channels=64,
+        in_index=4,
+        loss_decode=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+        norm_cfg=dict(requires_grad=True, type='SyncBN'),
+        num_classes=2,
+        num_convs=1,
+        type='FCNHead'),
+    pretrained=None,
+    test_cfg=dict(crop_size=(
+        128,
+        128,
+    ), mode='slide', stride=(
+        85,
+        85,
+    )),
+    train_cfg=dict(),
+    type='EncoderDecoder')
+norm_cfg = dict(requires_grad=True, type='SyncBN')
+optim_wrapper = dict(
+    clip_grad=None,
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
+    type='OptimWrapper')
+optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=40000,
+        eta_min=0.0001,
+        power=0.9,
+        type='PolyLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        data_root='data/CHASE_DB1',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                960,
+                999,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='ChaseDB1Dataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mDice',
+    ], type='IoUMetric')
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        960,
+        999,
+    ), type='Resize'),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs'),
+]
+train_cfg = dict(max_iters=40000, type='IterBasedTrainLoop', val_interval=4000)
+train_dataloader = dict(
+    batch_size=4,
+    dataset=dict(
+        dataset=dict(
+            data_prefix=dict(
+                img_path='images/training',
+                seg_map_path='annotations/training'),
+            data_root='data/CHASE_DB1',
+            pipeline=[
+                dict(type='LoadImageFromFile'),
+                dict(type='LoadAnnotations'),
+                dict(
+                    keep_ratio=True,
+                    ratio_range=(
+                        0.5,
+                        2.0,
+                    ),
+                    scale=(
+                        960,
+                        999,
+                    ),
+                    type='RandomResize'),
+                dict(
+                    cat_max_ratio=0.75,
+                    crop_size=(
+                        128,
+                        128,
+                    ),
+                    type='RandomCrop'),
+                dict(prob=0.5, type='RandomFlip'),
+                dict(type='PhotoMetricDistortion'),
+                dict(type='PackSegInputs'),
+            ],
+            type='ChaseDB1Dataset'),
+        times=40000,
+        type='RepeatDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='InfiniteSampler'))
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        keep_ratio=True,
+        ratio_range=(
+            0.5,
+            2.0,
+        ),
+        scale=(
+            960,
+            999,
+        ),
+        type='RandomResize'),
+    dict(cat_max_ratio=0.75, crop_size=(
+        128,
+        128,
+    ), type='RandomCrop'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs'),
+]
+tta_model = dict(type='SegTTAModel')
+tta_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(
+        transforms=[
+            [
+                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
+            ],
+            [
+                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
+                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
+            ],
+            [
+                dict(type='LoadAnnotations'),
+            ],
+            [
+                dict(type='PackSegInputs'),
+            ],
+        ],
+        type='TestTimeAug'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        data_root='data/CHASE_DB1',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                960,
+                999,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='ChaseDB1Dataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mDice',
+    ], type='IoUMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = 'mmseg_logs/chase_db1/'