diff --git a/configs/_base_/datasets/loveda.py b/configs/_base_/datasets/loveda.py
new file mode 100644
index 0000000000..e553356959
--- /dev/null
+++ b/configs/_base_/datasets/loveda.py
@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'LoveDADataset'
+data_root = 'data/loveDA'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=True),
+    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1024, 1024),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/train',
+        ann_dir='ann_dir/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/val',
+        ann_dir='ann_dir/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img_dir/val',
+        ann_dir='ann_dir/val',
+        pipeline=test_pipeline))
diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md
index c3e4340919..7efff620cc 100644
--- a/configs/deeplabv3plus/README.md
+++ b/configs/deeplabv3plus/README.md
@@ -85,6 +85,14 @@
 | DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | -           | 52.86 |         54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
 | DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 53.2 |         54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
 
+#### LoveDA
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.93      | 25.57             | 50.28 |         50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.37      | 6.00             | 50.99 |         50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.84       | 4.33          | 51.47 |         51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
+
 Note:
 
 - `FP16` means Mixed Precision (FP16) is adopted in training.
diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml
index 7b54f5003e..93210ddc30 100644
--- a/configs/deeplabv3plus/deeplabv3plus.yml
+++ b/configs/deeplabv3plus/deeplabv3plus.yml
@@ -599,3 +599,69 @@ Models:
       mIoU(ms+flip): 54.67
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
+- Name: deeplabv3plus_r18-d8_512x512_80k_loveda
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 39.11
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 1.93
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.28
+      mIoU(ms+flip): 50.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
+- Name: deeplabv3plus_r50-d8_512x512_80k_loveda
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 166.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.37
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.99
+      mIoU(ms+flip): 50.65
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
+- Name: deeplabv3plus_r101-d8_512x512_80k_loveda
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 230.95
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 10.84
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 51.47
+      mIoU(ms+flip): 51.32
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..b3ad3cae2b
--- /dev/null
+++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
@@ -0,0 +1,6 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))
diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..11fe640234
--- /dev/null
+++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
@@ -0,0 +1,13 @@
+_base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')),
+    decode_head=dict(
+        c1_in_channels=64,
+        c1_channels=12,
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..62756f65ba
--- /dev/null
+++ b/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/loveda.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=7), auxiliary_head=dict(num_classes=7))
diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
index 61fb56ea0e..2babf2f574 100644
--- a/configs/hrnet/README.md
+++ b/configs/hrnet/README.md
@@ -73,3 +73,11 @@
 | ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | FCN    | HRNetV2p-W48 | 480x480   |   40000 | -     | -           | 50.33 |         52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) |
 | FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | 51.12 |         53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) |
+
+#### LoveDA
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN | HRNetV2p-W18-Small  | 512x512   |   80000 | 1.72      | 30.07             | 49.3 |         49.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211105_180825-41dcc5dc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211105_180825.log.json)     |
+| FCN | HRNetV2p-W18  | 512x512   |   80000 | 2.90      | 16.77             | 50.87 |         51.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211105_165542-95be4d2b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211105_165542.log.json)     |
+| FCN | HRNetV2p-W48 | 512x512   |   80000 | 6.25       | 9.09          | 51.04 |         51.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211105_131509-f07e47c6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211105_131509.log.json) |
diff --git a/configs/hrnet/fcn_hr18_512x512_80k_loveda.py b/configs/hrnet/fcn_hr18_512x512_80k_loveda.py
new file mode 100644
index 0000000000..f7bc764f8f
--- /dev/null
+++ b/configs/hrnet/fcn_hr18_512x512_80k_loveda.py
@@ -0,0 +1,4 @@
+_base_ = [
+    '../_base_/models/fcn_hr18.py', '../_base_/datasets/loveda.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
diff --git a/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py b/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
new file mode 100644
index 0000000000..b39769ffc2
--- /dev/null
+++ b/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
@@ -0,0 +1,11 @@
+_base_ = './fcn_hr18_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://msra/hrnetv2_w18_small'),
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
diff --git a/configs/hrnet/fcn_hr48_512x512_80k_loveda.py b/configs/hrnet/fcn_hr48_512x512_80k_loveda.py
new file mode 100644
index 0000000000..269dbf662d
--- /dev/null
+++ b/configs/hrnet/fcn_hr48_512x512_80k_loveda.py
@@ -0,0 +1,11 @@
+_base_ = './fcn_hr18_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w48'),
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml
index c4a13f7a65..885ed3183f 100644
--- a/configs/hrnet/hrnet.yml
+++ b/configs/hrnet/hrnet.yml
@@ -447,3 +447,69 @@ Models:
       mIoU(ms+flip): 53.56
   Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
+- Name: fcn_hr18s_512x512_80k_loveda
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 33.26
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 1.72
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 49.3
+      mIoU(ms+flip): 49.23
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211105_180825-41dcc5dc.pth
+- Name: fcn_hr18_512x512_80k_loveda
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 59.63
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 2.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 50.87
+      mIoU(ms+flip): 51.24
+  Config: configs/hrnet/fcn_hr18_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211105_165542-95be4d2b.pth
+- Name: fcn_hr48_512x512_80k_loveda
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 110.01
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.25
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 51.04
+      mIoU(ms+flip): 51.12
+  Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211105_131509-f07e47c6.pth
diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md
index ad7fcdf4cc..995777f849 100644
--- a/configs/pspnet/README.md
+++ b/configs/pspnet/README.md
@@ -98,21 +98,29 @@ We support evaluation results on these two datasets using models above trained o
 
 | Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                       |
 | --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   20000 | 9.6      | 20.5          | 35.69 |         36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json)         |
-| PSPNet | R-101-D8 | 512x512   |   20000 | 13.2     | 11.1          | 37.26 |         38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |  40000 | -        | -              | 36.33 |         37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  40000 | -        | -              | 37.76 |         38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
+| PSPNet | R-50-D8  | 512x512   |   20000 | 9.6      | 20.5          | 35.69 |         36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json)         |
+| PSPNet | R-101-D8 | 512x512   |   20000 | 13.2     | 11.1          | 37.26 |         38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |  40000 | -        | -              | 36.33 |         37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  40000 | -        | -              | 37.76 |         38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
 
 ### COCO-Stuff 164k
 
 | Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                       |
 | --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 9.6      | 20.5          | 38.80 |         39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)         |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 13.2     | 11.1          | 40.34 |         40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | 39.64 |         39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | 41.28 |         41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
-| PSPNet | R-50-D8  | 512x512   |  320000 | -        | -              | 40.53 |         40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  320000 | -        | -              | 41.95 |         42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 9.6      | 20.5          | 38.80 |         39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)         |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 13.2     | 11.1          | 40.34 |         40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | 39.64 |         39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | 41.28 |         41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
+| PSPNet | R-50-D8  | 512x512   |  320000 | -        | -              | 40.53 |         40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  320000 | -        | -              | 41.95 |         42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
+
+#### LoveDA
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| PSPNet | R-18-D8  | 512x512   |   80000 | 1.45      | 26.87             | 48.62 |         47.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14      | 6.60             | 50.46 |         50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61       | 4.58          | 51.86 |         51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
 
 Note:
 
diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml
index 1a46b4632a..c951269cec 100644
--- a/configs/pspnet/pspnet.yml
+++ b/configs/pspnet/pspnet.yml
@@ -741,3 +741,69 @@ Models:
       mIoU(ms+flip): 42.42
   Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
+- Name: pspnet_r18-d8_512x512_80k_loveda
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 37.22
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 1.45
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 48.62
+      mIoU(ms+flip): 47.57
+  Config: configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
+- Name: pspnet_r50-d8_512x512_80k_loveda
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 151.52
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.14
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 50.46
+      mIoU(ms+flip): 50.19
+  Config: configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
+- Name: pspnet_r101-d8_512x512_80k_loveda
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 218.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.61
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 51.86
+      mIoU(ms+flip): 51.34
+  Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
diff --git a/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..03c0251f6c
--- /dev/null
+++ b/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
@@ -0,0 +1,6 @@
+_base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))
diff --git a/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..dbb832b244
--- /dev/null
+++ b/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
@@ -0,0 +1,11 @@
+_base_ = './pspnet_r50-d8_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')),
+    decode_head=dict(
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
new file mode 100644
index 0000000000..830af482ef
--- /dev/null
+++ b/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/loveda.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=7), auxiliary_head=dict(num_classes=7))
diff --git a/docs/dataset_prepare.md b/docs/dataset_prepare.md
index 468eceac2b..70ab9b3f38 100644
--- a/docs/dataset_prepare.md
+++ b/docs/dataset_prepare.md
@@ -108,6 +108,14 @@ mmsegmentation
 |   |   └── leftImg8bit
 |   |   |   └── test
 |   |   |       └── night
+│   ├── loveDA
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
 ```
 
 ### Cityscapes
@@ -253,3 +261,28 @@ Since we only support test models on this dataset, you may only download [the va
 ### Nighttime Driving
 
 Since we only support test models on this dataset, you may only download [the test set](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip).
+
+### LoveDA
+
+The data could be downloaded from Google Drive [here](https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing).
+
+Or it can be downloaded from [zenodo](https://zenodo.org/record/5706578#.YZvN7SYRXdF), you should run the following command:
+
+```shell
+# Download Train.zip
+wget https://zenodo.org/record/5706578/files/Train.zip
+# Download Val.zip
+wget https://zenodo.org/record/5706578/files/Val.zip
+# Download Test.zip
+wget https://zenodo.org/record/5706578/files/Test.zip
+```
+
+For LoveDA dataset, please run the following command to download and re-organize the dataset.
+
+```shell
+python tools/convert_datasets/loveda.py /path/to/loveDA
+```
+
+Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/inference.md).
+
+More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA).
diff --git a/docs/inference.md b/docs/inference.md
index 632400d343..863a8a3f3d 100644
--- a/docs/inference.md
+++ b/docs/inference.md
@@ -101,3 +101,25 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
     ```
 
     Using ```pmap``` to view CPU memory footprint, it used 2.25GB CPU memory with ```efficient_test=True``` and 11.06GB CPU memory with ```efficient_test=False``` . This optional parameter can save a lot of memory. (After mmseg v0.17, efficient_test has not effect and we use a progressive mode to evaluation and format results efficiently by default.)
+
+7. Test PSPNet on LoveDA test split with 1 GPU, and generate the png files to be submit to the official evaluation server.
+
+   First, add following to config file `configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py`,
+
+    ```python
+    data = dict(
+        test=dict(
+            img_dir='img_dir/test',
+            ann_dir='ann_dir/test'))
+    ```
+
+   Then run test.
+
+    ```shell
+   python ./tools/test.py configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py \
+        checkpoints/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth \
+        --format-only --eval-options "imgfile_prefix=./pspnet_test_results"
+    ```
+
+   You will get png files under `./pspnet_test_results` directory.
+   You may run `zip -r -j Results.zip pspnet_test_results/` and submit the zip file to [evaluation server](https://competitions.codalab.org/competitions/35865#participate-submit_results).
diff --git a/docs_zh-CN/dataset_prepare.md b/docs_zh-CN/dataset_prepare.md
index 72fed1ccda..da50febeb0 100644
--- a/docs_zh-CN/dataset_prepare.md
+++ b/docs_zh-CN/dataset_prepare.md
@@ -89,6 +89,14 @@ mmsegmentation
 |   |   └── leftImg8bit
 |   |   |   └── test
 |   |   |       └── night
+│   ├── loveDA
+│   │   ├── img_dir
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   │   ├── test
+│   │   ├── ann_dir
+│   │   │   ├── train
+│   │   │   ├── val
 ```
 
 ### Cityscapes
@@ -190,8 +198,33 @@ python tools/convert_datasets/stare.py /path/to/stare-images.tar /path/to/labels
 
 ### Dark Zurich
 
-因为我们只支持在此数据集上测试模型，所以您只需下载[验证集](https://data.vision.ee.ethz.ch/csakarid/shared/GCMA_UIoU/Dark_Zurich_val_anon.zip)。
+因为我们只支持在此数据集上测试模型，所以您只需下载[验证集](https://data.vision.ee.ethz.ch/csakarid/shared/GCMA_UIoU/Dark_Zurich_val_anon.zip) 。
 
 ### Nighttime Driving
 
-因为我们只支持在此数据集上测试模型，所以您只需下载[测试集](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip)。
+因为我们只支持在此数据集上测试模型，所以您只需下载[测试集](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip) 。
+
+### LoveDA
+
+可以从 Google Drive 里下载 [LoveDA数据集](https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing) 。
+
+或者它还可以从 [zenodo](https://zenodo.org/record/5706578#.YZvN7SYRXdF) 下载, 您需要运行如下命令:
+
+```shell
+# Download Train.zip
+wget https://zenodo.org/record/5706578/files/Train.zip
+# Download Val.zip
+wget https://zenodo.org/record/5706578/files/Val.zip
+# Download Test.zip
+wget https://zenodo.org/record/5706578/files/Test.zip
+```
+
+对于 LoveDA 数据集，请运行以下命令下载并重新组织数据集
+
+```shell
+python tools/convert_datasets/loveda.py /path/to/loveDA
+```
+
+请参照 [这里](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/inference.md) 来使用训练好的模型去预测 LoveDA 测试集并且提交到官网。
+
+关于 LoveDA 的更多细节可以在[这里](https://github.com/Junjue-Wang/LoveDA) 找到。
diff --git a/docs_zh-CN/inference.md b/docs_zh-CN/inference.md
index 7d14bb980d..7dad83471e 100644
--- a/docs_zh-CN/inference.md
+++ b/docs_zh-CN/inference.md
@@ -84,7 +84,7 @@ python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}]
     ```
 
    您会在文件夹 `./pspnet_test_results` 里得到生成的 png 文件。
-   您也许可以运行 `zip -r results.zip pspnet_test_results/` 并提交 zip 文件给 [evaluation server](https://www.cityscapes-dataset.com/submit/)。
+   您也许可以运行 `zip -r results.zip pspnet_test_results/` 并提交 zip 文件给 [evaluation server](https://www.cityscapes-dataset.com/submit/) 。
 
 6. 在 Cityscapes 数据集上使用 CPU 高效内存选项来测试 DeeplabV3+ `mIoU` 指标 (没有保存测试结果)
 
@@ -97,3 +97,25 @@ python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}]
     ```
 
     使用 ```pmap``` 可查看 CPU 内存情况,  ```efficient_test=True``` 会使用约 2.25GB 的 CPU 内存， ```efficient_test=False``` 会使用约 11.06GB 的 CPU 内存。 这个可选参数可以节约很多 CPU 内存。（MMseg v0.17 之后, `efficient_test` 参数将不再生效, 我们使用了一种渐近的方式来更加有效快速地评估和保存结果。）
+
+7. 在 LoveDA 数据集上1卡 GPU 测试 PSPNet， 并生成 png 文件以便提交给官方评估服务器
+
+   首先，在配置文件里添加内容： `configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py`,
+
+    ```python
+    data = dict(
+        test=dict(
+            img_dir='img_dir/test',
+            ann_dir='ann_dir/test'))
+    ```
+
+   随后，进行测试。
+
+    ```shell
+   python ./tools/test.py configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py \
+        checkpoints/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth \
+        --format-only --eval-options "imgfile_prefix=./pspnet_test_results"
+    ```
+
+   您会在文件夹 `./pspnet_test_results` 里得到生成的 png 文件。
+   您也许可以运行 `zip -r -j Results.zip pspnet_test_results/` 并提交 zip 文件给 [evaluation server](https://competitions.codalab.org/competitions/35865#participate-submit_results) 。
diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py
index 4b8e124cf8..c115ab796f 100644
--- a/mmseg/datasets/__init__.py
+++ b/mmseg/datasets/__init__.py
@@ -9,6 +9,7 @@
 from .dataset_wrappers import ConcatDataset, RepeatDataset
 from .drive import DRIVEDataset
 from .hrf import HRFDataset
+from .loveda import LoveDADataset
 from .night_driving import NightDrivingDataset
 from .pascal_context import PascalContextDataset, PascalContextDataset59
 from .stare import STAREDataset
@@ -20,5 +21,5 @@
     'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
     'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
     'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
-    'COCOStuffDataset'
+    'COCOStuffDataset', 'LoveDADataset'
 ]
diff --git a/mmseg/datasets/ade.py b/mmseg/datasets/ade.py
index d807a001a0..db94cebd3b 100644
--- a/mmseg/datasets/ade.py
+++ b/mmseg/datasets/ade.py
@@ -94,7 +94,7 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
         """Write the segmentation results to images.
 
         Args:
-            results (list[list | tuple | ndarray]): Testing results of the
+            results (list[ndarray]): Testing results of the
                 dataset.
             imgfile_prefix (str): The filename prefix of the png files.
                 If the prefix is "somepath/xxx",
diff --git a/mmseg/datasets/cityscapes.py b/mmseg/datasets/cityscapes.py
index 2be00d6848..ed633d00db 100644
--- a/mmseg/datasets/cityscapes.py
+++ b/mmseg/datasets/cityscapes.py
@@ -52,7 +52,7 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
         """Write the segmentation results to images.
 
         Args:
-            results (list[list | tuple | ndarray]): Testing results of the
+            results (list[ndarray]): Testing results of the
                 dataset.
             imgfile_prefix (str): The filename prefix of the png files.
                 If the prefix is "somepath/xxx",
diff --git a/mmseg/datasets/loveda.py b/mmseg/datasets/loveda.py
new file mode 100644
index 0000000000..90d654f625
--- /dev/null
+++ b/mmseg/datasets/loveda.py
@@ -0,0 +1,92 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import numpy as np
+from PIL import Image
+
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class LoveDADataset(CustomDataset):
+    """LoveDA dataset.
+
+    In segmentation map annotation for LoveDA, 0 is the ignore index.
+    ``reduce_zero_label`` should be set to True. The ``img_suffix`` and
+    ``seg_map_suffix`` are both fixed to '.png'.
+    """
+    CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest',
+               'agricultural')
+
+    PALETTE = [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
+               [159, 129, 183], [0, 255, 0], [255, 195, 128]]
+
+    def __init__(self, **kwargs):
+        super(LoveDADataset, self).__init__(
+            img_suffix='.png',
+            seg_map_suffix='.png',
+            reduce_zero_label=True,
+            **kwargs)
+
+    def results2img(self, results, imgfile_prefix, indices=None):
+        """Write the segmentation results to images.
+
+        Args:
+            results (list[ndarray]): Testing results of the
+                dataset.
+            imgfile_prefix (str): The filename prefix of the png files.
+                If the prefix is "somepath/xxx",
+                the png files will be named "somepath/xxx.png".
+            indices (list[int], optional): Indices of input results, if not
+                set, all the indices of the dataset will be used.
+                Default: None.
+
+        Returns:
+            list[str: str]: result txt files which contains corresponding
+            semantic segmentation images.
+        """
+
+        mmcv.mkdir_or_exist(imgfile_prefix)
+        result_files = []
+        for result, idx in zip(results, indices):
+
+            filename = self.img_infos[idx]['filename']
+            basename = osp.splitext(osp.basename(filename))[0]
+
+            png_filename = osp.join(imgfile_prefix, f'{basename}.png')
+
+            # The  index range of official requirement is from 0 to 6.
+            output = Image.fromarray(result.astype(np.uint8))
+            output.save(png_filename)
+            result_files.append(png_filename)
+
+        return result_files
+
+    def format_results(self, results, imgfile_prefix, indices=None):
+        """Format the results into dir (standard format for LoveDA evaluation).
+
+        Args:
+            results (list): Testing results of the dataset.
+            imgfile_prefix (str): The prefix of images files. It
+                includes the file path and the prefix of filename, e.g.,
+                "a/b/prefix".
+            indices (list[int], optional): Indices of input results,
+                if not set, all the indices of the dataset will be used.
+                Default: None.
+
+        Returns:
+            tuple: (result_files, tmp_dir), result_files is a list containing
+                the image paths, tmp_dir is the temporal directory created
+                for saving json/png files when img_prefix is not specified.
+        """
+        if indices is None:
+            indices = list(range(len(self)))
+
+        assert isinstance(results, list), 'results must be a list.'
+        assert isinstance(indices, list), 'indices must be a list.'
+
+        result_files = self.results2img(results, imgfile_prefix, indices)
+
+        return result_files
diff --git a/tests/data/pseudo_loveda_dataset/ann_dir/0.png b/tests/data/pseudo_loveda_dataset/ann_dir/0.png
new file mode 100644
index 0000000000..7823fd6717
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/ann_dir/0.png differ
diff --git a/tests/data/pseudo_loveda_dataset/ann_dir/1.png b/tests/data/pseudo_loveda_dataset/ann_dir/1.png
new file mode 100644
index 0000000000..bc50ac11ec
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/ann_dir/1.png differ
diff --git a/tests/data/pseudo_loveda_dataset/ann_dir/2.png b/tests/data/pseudo_loveda_dataset/ann_dir/2.png
new file mode 100644
index 0000000000..c182838513
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/ann_dir/2.png differ
diff --git a/tests/data/pseudo_loveda_dataset/img_dir/0.png b/tests/data/pseudo_loveda_dataset/img_dir/0.png
new file mode 100644
index 0000000000..03a0652396
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/img_dir/0.png differ
diff --git a/tests/data/pseudo_loveda_dataset/img_dir/1.png b/tests/data/pseudo_loveda_dataset/img_dir/1.png
new file mode 100644
index 0000000000..2fe837f228
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/img_dir/1.png differ
diff --git a/tests/data/pseudo_loveda_dataset/img_dir/2.png b/tests/data/pseudo_loveda_dataset/img_dir/2.png
new file mode 100644
index 0000000000..b824499402
Binary files /dev/null and b/tests/data/pseudo_loveda_dataset/img_dir/2.png differ
diff --git a/tests/test_data/test_dataset.py b/tests/test_data/test_dataset.py
index 65244192c9..455e82dbdf 100644
--- a/tests/test_data/test_dataset.py
+++ b/tests/test_data/test_dataset.py
@@ -13,8 +13,8 @@
 
 from mmseg.core.evaluation import get_classes, get_palette
 from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
-                            ConcatDataset, CustomDataset, PascalVOCDataset,
-                            RepeatDataset, build_dataset)
+                            ConcatDataset, CustomDataset, LoveDADataset,
+                            PascalVOCDataset, RepeatDataset, build_dataset)
 
 
 def test_classes():
@@ -622,6 +622,32 @@ def test_concat_cityscapes(separate_eval):
                           separate_eval=separate_eval)
 
 
+def test_loveda():
+    test_dataset = LoveDADataset(
+        pipeline=[],
+        img_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_loveda_dataset/img_dir'),
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_loveda_dataset/ann_dir'))
+    assert len(test_dataset) == 3
+
+    gt_seg_maps = list(test_dataset.get_gt_seg_maps())
+
+    # Test format_results
+    pseudo_results = []
+    for idx in range(len(test_dataset)):
+        h, w = gt_seg_maps[idx].shape
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    file_paths = test_dataset.format_results(pseudo_results, '.format_loveda')
+    assert len(file_paths) == len(test_dataset)
+    # Test loveda evaluate
+
+    test_dataset.evaluate(
+        pseudo_results, metric='mIoU', imgfile_prefix='.format_loveda')
+
+    shutil.rmtree('.format_loveda')
+
+
 @patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
 @patch('mmseg.datasets.CustomDataset.__getitem__',
        MagicMock(side_effect=lambda idx: idx))
diff --git a/tools/convert_datasets/loveda.py b/tools/convert_datasets/loveda.py
new file mode 100644
index 0000000000..3a0626837b
--- /dev/null
+++ b/tools/convert_datasets/loveda.py
@@ -0,0 +1,73 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import shutil
+import tempfile
+import zipfile
+
+import mmcv
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert LoveDA dataset to mmsegmentation format')
+    parser.add_argument('dataset_path', help='LoveDA folder path')
+    parser.add_argument('--tmp_dir', help='path of the temporary directory')
+    parser.add_argument('-o', '--out_dir', help='output path')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    dataset_path = args.dataset_path
+    if args.out_dir is None:
+        out_dir = osp.join('data', 'loveDA')
+    else:
+        out_dir = args.out_dir
+
+    print('Making directories...')
+    mmcv.mkdir_or_exist(out_dir)
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
+
+    assert 'Train.zip' in os.listdir(dataset_path), \
+        'Train.zip is not in {}'.format(dataset_path)
+    assert 'Val.zip' in os.listdir(dataset_path), \
+        'Val.zip is not in {}'.format(dataset_path)
+    assert 'Test.zip' in os.listdir(dataset_path), \
+        'Test.zip is not in {}'.format(dataset_path)
+
+    with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
+        for dataset in ['Train', 'Val', 'Test']:
+            zip_file = zipfile.ZipFile(
+                os.path.join(dataset_path, dataset + '.zip'))
+            zip_file.extractall(tmp_dir)
+            data_type = dataset.lower()
+            for location in ['Rural', 'Urban']:
+                for image_type in ['images_png', 'masks_png']:
+                    if image_type == 'images_png':
+                        dst = osp.join(out_dir, 'img_dir', data_type)
+                    else:
+                        dst = osp.join(out_dir, 'ann_dir', data_type)
+                    if dataset == 'Test' and image_type == 'masks_png':
+                        continue
+                    else:
+                        src_dir = osp.join(tmp_dir, dataset, location,
+                                           image_type)
+                        src_lst = os.listdir(src_dir)
+                        for file in src_lst:
+                            shutil.move(osp.join(src_dir, file), dst)
+        print('Removing the temporary files...')
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()