diff --git a/.dev/md2yml.py b/.dev/md2yml.py
index 7c7cc95d7..1d68498db 100755
--- a/.dev/md2yml.py
+++ b/.dev/md2yml.py
@@ -12,11 +12,20 @@ import os.path as osp
 import re
 import sys
 
-import mmcv
 from lxml import etree
+from mmcv.fileio import dump
 
 MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
 
+COLLECTIONS = [
+    'ANN', 'APCNet', 'BiSeNetV1', 'BiSeNetV2', 'CCNet', 'CGNet', 'DANet',
+    'DeepLabV3', 'DeepLabV3+', 'DMNet', 'DNLNet', 'DPT', 'EMANet', 'EncNet',
+    'ERFNet', 'FastFCN', 'FastSCNN', 'FCN', 'GCNet', 'ICNet', 'ISANet', 'KNet',
+    'NonLocalNet', 'OCRNet', 'PointRend', 'PSANet', 'PSPNet', 'Segformer',
+    'Segmenter', 'FPN', 'SETR', 'STDC', 'UNet', 'UPerNet'
+]
+COLLECTIONS_TEMP = []
+
 
 def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
     """Dump object to a yaml file, and check if the file content is different
@@ -30,7 +39,7 @@ def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
         Bool: If the target YAML file is different from the original.
     """
 
-    str_dump = mmcv.dump(obj, None, file_format='yaml', sort_keys=sort_keys)
+    str_dump = dump(obj, None, file_format='yaml', sort_keys=sort_keys)
     if osp.isfile(filename):
         file_exists = True
         with open(filename, 'r', encoding='utf-8') as f:
@@ -131,7 +140,6 @@ def parse_md(md_file):
                   and lines[i + 1][:3] == '| -' and 'Method' in line
                   and 'Crop Size' in line and 'Mem (GB)' in line):
                 cols = [col.strip() for col in line.split('|')]
-                print(cols)
                 method_id = cols.index('Method')
                 backbone_id = cols.index('Backbone')
                 crop_size_id = cols.index('Crop Size')
@@ -248,11 +256,21 @@ def parse_md(md_file):
                     collection.pop(check_key)
                 else:
                     collection[check_key].pop(key)
-    if is_backbone:
-        result = {'Models': models}
-    else:
-        result = {'Collections': [collection], 'Models': models}
     yml_file = f'{md_file[:-9]}{collection_name}.yml'
+    if is_backbone:
+        if collection['Name'] not in COLLECTIONS:
+            result = {
+                'Collections': [collection],
+                'Models': models,
+                'Yml': yml_file
+            }
+            COLLECTIONS_TEMP.append(result)
+            return False
+        else:
+            result = {'Models': models}
+    else:
+        COLLECTIONS.append(collection['Name'])
+        result = {'Collections': [collection], 'Models': models}
     return dump_yaml_and_check_difference(result, yml_file)
 
 
@@ -288,6 +306,12 @@ if __name__ == '__main__':
     for fn in file_list:
         file_modified |= parse_md(fn)
 
-    file_modified |= update_model_index()
+    for result in COLLECTIONS_TEMP:
+        collection = result['Collections'][0]
+        yml_file = result.pop('Yml', None)
+        if collection['Name'] in COLLECTIONS:
+            result.pop('Collections')
+        file_modified |= dump_yaml_and_check_difference(result, yml_file)
 
+    file_modified |= update_model_index()
     sys.exit(1 if file_modified else 0)
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0de24643b..d38d7e83f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -48,7 +48,7 @@ repos:
         name: update-model-index
         description: Collect model information and update model-index.yml
         entry: .dev/md2yml.py
-        additional_dependencies: [mmcv, lxml]
+        additional_dependencies: [mmcv, lxml, opencv-python]
         language: python
         files: ^configs/.*\.md$
         require_serial: true
diff --git a/configs/beit/README.md b/configs/beit/README.md
index bdd434e70..31e1bd6a8 100644
--- a/configs/beit/README.md
+++ b/configs/beit/README.md
@@ -81,5 +81,5 @@ upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth $GPUS --eval mIoU
 
 | Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                                 |
 | ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ---------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UperNet | BEiT-B   | 640x640   | ImageNet-22K | 224x224           | 16         | 160000  | 15.88    | 2.00           | 53.08 |         53.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json)                         |
-| UperNet | BEiT-L   | 640x640   | ImageNet-22K | 224x224           | 8          | 320000  | 22.64    | 0.96           | 56.33 |         56.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json) |
+| UPerNet | BEiT-B   | 640x640   | ImageNet-22K | 224x224           | 16         | 160000  | 15.88    | 2.00           | 53.08 |         53.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json)                         |
+| UPerNet | BEiT-L   | 640x640   | ImageNet-22K | 224x224           | 8          | 320000  | 22.64    | 0.96           | 56.33 |         56.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json) |
diff --git a/configs/beit/beit.yml b/configs/beit/beit.yml
index 6f3cee3ed..602a887d4 100644
--- a/configs/beit/beit.yml
+++ b/configs/beit/beit.yml
@@ -1,6 +1,6 @@
 Models:
 - Name: upernet_beit-base_8x2_640x640_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: BEiT-B
     crop size: (640,640)
@@ -22,7 +22,7 @@ Models:
   Config: configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth
 - Name: upernet_beit-large_fp16_8x1_640x640_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: BEiT-L
     crop size: (640,640)
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
index 84a8ae4d5..09eb702c7 100644
--- a/configs/convnext/README.md
+++ b/configs/convnext/README.md
@@ -52,7 +52,7 @@ The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on t
 | ConvNeXt-L\*  | ImageNet-21k  |  197.77   |  34.37   |       [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth)        |
 | ConvNeXt-XL\* | ImageNet-21k  |  350.20   |  60.93   |       [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-xlarge_3rdparty_in21k_20220301-08aa5ddc.pth)       |
 
-*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt/tree/main/semantic_segmentation#results-and-fine-tuned-models).*
+*Models with* are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt/tree/main/semantic_segmentation#results-and-fine-tuned-models).\*
 
 ## Results and models
 
@@ -60,12 +60,12 @@ The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on t
 
 | Method  | Backbone    | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                                                                             |
 | ------- | ----------- | --------- | ------- | -------- | -------------- | ----- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UperNet | ConvNeXt-T  | 512x512   | 160000  | 4.23     | 19.90          | 46.11 | 46.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json)         |
-| UperNet | ConvNeXt-S  | 512x512   | 160000  | 5.16     | 15.18          | 48.56 | 49.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json)     |
-| UperNet | ConvNeXt-B  | 512x512   | 160000  | 6.33     | 14.41          | 48.71 | 49.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json)         |
-| UperNet | ConvNeXt-B  | 640x640   | 160000  | 8.53     | 10.88          | 52.13 | 52.66         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json)         |
-| UperNet | ConvNeXt-L  | 640x640   | 160000  | 12.08    | 7.69           | 53.16 | 53.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json)     |
-| UperNet | ConvNeXt-XL | 640x640   | 160000  | 26.16\*  | 6.33           | 53.58 | 54.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) |
+| UPerNet | ConvNeXt-T  | 512x512   | 160000  | 4.23     | 19.90          | 46.11 | 46.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json)         |
+| UPerNet | ConvNeXt-S  | 512x512   | 160000  | 5.16     | 15.18          | 48.56 | 49.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json)     |
+| UPerNet | ConvNeXt-B  | 512x512   | 160000  | 6.33     | 14.41          | 48.71 | 49.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json)         |
+| UPerNet | ConvNeXt-B  | 640x640   | 160000  | 8.53     | 10.88          | 52.13 | 52.66         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json)         |
+| UPerNet | ConvNeXt-L  | 640x640   | 160000  | 12.08    | 7.69           | 53.16 | 53.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json)     |
+| UPerNet | ConvNeXt-XL | 640x640   | 160000  | 26.16\*  | 6.33           | 53.58 | 54.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) |
 
 Note:
 
diff --git a/configs/convnext/convnext.yml b/configs/convnext/convnext.yml
index 3e521eff3..2b943aa15 100644
--- a/configs/convnext/convnext.yml
+++ b/configs/convnext/convnext.yml
@@ -1,6 +1,6 @@
 Models:
 - Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-T
     crop size: (512,512)
@@ -22,7 +22,7 @@ Models:
   Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
 - Name: upernet_convnext_small_fp16_512x512_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-S
     crop size: (512,512)
@@ -44,7 +44,7 @@ Models:
   Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
 - Name: upernet_convnext_base_fp16_512x512_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-B
     crop size: (512,512)
@@ -66,7 +66,7 @@ Models:
   Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
 - Name: upernet_convnext_base_fp16_640x640_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-B
     crop size: (640,640)
@@ -88,7 +88,7 @@ Models:
   Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
 - Name: upernet_convnext_large_fp16_640x640_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-L
     crop size: (640,640)
@@ -110,7 +110,7 @@ Models:
   Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
 - Name: upernet_convnext_xlarge_fp16_640x640_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ConvNeXt-XL
     crop size: (640,640)
diff --git a/configs/knet/README.md b/configs/knet/README.md
index cad14a6ea..a51c5cbcf 100644
--- a/configs/knet/README.md
+++ b/configs/knet/README.md
@@ -40,10 +40,10 @@ Semantic, instance, and panoptic segmentations have been addressed using differe
 | KNet + FCN       | R-50-D8  | 512x512   | 80000   | 7.01     | 19.24          | 43.60 | 45.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json)                         |
 | KNet + PSPNet    | R-50-D8  | 512x512   | 80000   | 6.98     | 20.04          | 44.18 | 45.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json)             |
 | KNet + DeepLabV3 | R-50-D8  | 512x512   | 80000   | 7.42     | 12.10          | 45.06 | 46.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json) |
-| KNet + UperNet   | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)         |
-| KNet + UperNet   | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)         |
-| KNet + UperNet   | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)         |
-| KNet + UperNet   | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)         |
+| KNet + UPerNet   | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)         |
+| KNet + UPerNet   | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)         |
+| KNet + UPerNet   | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)         |
+| KNet + UPerNet   | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)         |
 
 Note:
 
diff --git a/configs/mae/README.md b/configs/mae/README.md
index 8a184f0ce..562f6f8bf 100644
--- a/configs/mae/README.md
+++ b/configs/mae/README.md
@@ -79,4 +79,4 @@ upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth $GPUS
 
 | Method  | Backbone | Crop Size | pretrain    | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                                       |
 | ------- | -------- | --------- | ----------- | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UperNet | ViT-B    | 512x512   | ImageNet-1K | 224x224           | 16         | 160000  | 9.96     | 7.14           | 48.13 |         48.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752.log.json) |
+| UPerNet | ViT-B    | 512x512   | ImageNet-1K | 224x224           | 16         | 160000  | 9.96     | 7.14           | 48.13 |         48.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752.log.json) |
diff --git a/configs/mae/mae.yml b/configs/mae/mae.yml
index 5a869344e..d78f99c86 100644
--- a/configs/mae/mae.yml
+++ b/configs/mae/mae.yml
@@ -1,6 +1,6 @@
 Models:
 - Name: upernet_mae-base_fp16_8x2_512x512_160k_ade20k
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: ViT-B
     crop size: (512,512)
diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md
index eebafdc95..66f20688b 100644
--- a/configs/mobilenet_v3/README.md
+++ b/configs/mobilenet_v3/README.md
@@ -6,6 +6,8 @@
 
 <!-- [BACKBONE] -->
 
+<!-- [ALGORITHM] -->
+
 <a href="https://github.com/tensorflow/models/tree/master/research/deeplab">Official Repo</a>
 
 <a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15">Code Snippet</a>
diff --git a/configs/mobilenet_v3/mobilenet_v3.yml b/configs/mobilenet_v3/mobilenet_v3.yml
index 81a179647..003cbe530 100644
--- a/configs/mobilenet_v3/mobilenet_v3.yml
+++ b/configs/mobilenet_v3/mobilenet_v3.yml
@@ -1,3 +1,17 @@
+Collections:
+- Name: LRASPP
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    URL: https://arxiv.org/abs/1905.02244
+    Title: Searching for MobileNetV3
+  README: configs/mobilenet_v3/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
 - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
   In Collection: LRASPP
diff --git a/configs/swin/README.md b/configs/swin/README.md
index bd6583f06..6b21b6d1b 100644
--- a/configs/swin/README.md
+++ b/configs/swin/README.md
@@ -68,9 +68,9 @@ In our default setting, pretrained models and their corresponding [original mode
 
 | Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
 | ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UperNet | Swin-T   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 5.02     | 21.06          | 44.41 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542.log.json)         |
-| UperNet | Swin-S   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 6.17     | 14.72          | 47.72 |         49.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015.log.json)     |
-| UperNet | Swin-B   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 7.61     | 12.65          | 47.99 |         49.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340.log.json)         |
-| UperNet | Swin-B   | 512x512   | ImageNet-22K | 224x224           | 16         | 160000  | -        | -              | 50.31 |          51.9 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650.log.json)     |
-| UperNet | Swin-B   | 512x512   | ImageNet-1K  | 384x384           | 16         | 160000  | 8.52     | 12.10          | 48.35 |         49.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020.log.json)     |
-| UperNet | Swin-B   | 512x512   | ImageNet-22K | 384x384           | 16         | 160000  | -        | -              | 50.76 |          52.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459.log.json) |
+| UPerNet | Swin-T   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 5.02     | 21.06          | 44.41 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542.log.json)         |
+| UPerNet | Swin-S   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 6.17     | 14.72          | 47.72 |         49.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 7.61     | 12.65          | 47.99 |         49.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340.log.json)         |
+| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 224x224           | 16         | 160000  | -        | -              | 50.31 |          51.9 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 384x384           | 16         | 160000  | 8.52     | 12.10          | 48.35 |         49.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 384x384           | 16         | 160000  | -        | -              | 50.76 |          52.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459.log.json) |
diff --git a/configs/swin/swin.yml b/configs/swin/swin.yml
index cf7c4651f..ef21d2165 100644
--- a/configs/swin/swin.yml
+++ b/configs/swin/swin.yml
@@ -1,6 +1,6 @@
 Models:
 - Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-T
     crop size: (512,512)
@@ -22,7 +22,7 @@ Models:
   Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
 - Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-S
     crop size: (512,512)
@@ -44,7 +44,7 @@ Models:
   Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
 - Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -66,7 +66,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
 - Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -80,7 +80,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
 - Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -102,7 +102,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
 - Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
-  In Collection: UperNet
+  In Collection: UPerNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)