diff --git a/.dev/md2yml.py b/.dev/md2yml.py
index 3f118c1..82368df 100755
--- a/.dev/md2yml.py
+++ b/.dev/md2yml.py
@@ -9,25 +9,28 @@
 import glob
 import os
 import os.path as osp
+import re
 import sys
 
 import mmcv
+from lxml import etree
 
 MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
 
 
-def dump_yaml_and_check_difference(obj, filename):
+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
     from the original.
 
     Args:
         obj (any): The python object to be dumped.
         filename (str): YAML filename to dump the object to.
+        sort_keys (str); Sort key by dictionary order.
     Returns:
         Bool: If the target YAML file is different from the original.
     """
 
-    str_dump = mmcv.dump(obj, None, file_format='yaml', sort_keys=True)
+    str_dump = mmcv.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:
@@ -54,12 +57,29 @@ def parse_md(md_file):
     Returns:
         Bool: If the target YAML file is different from the original.
     """
-    collection_name = osp.dirname(md_file).split('/')[-1]
+    collection_name = osp.split(osp.dirname(md_file))[1]
     configs = os.listdir(osp.dirname(md_file))
 
-    collection = dict(Name=collection_name, Metadata={'Training Data': []})
+    collection = dict(
+        Name=collection_name,
+        Metadata={'Training Data': []},
+        Paper={
+            'URL': '',
+            'Title': ''
+        },
+        README=md_file,
+        Code={
+            'URL': '',
+            'Version': ''
+        })
+    collection.update({'Converted From': {'Weights': '', 'Code': ''}})
     models = []
     datasets = []
+    paper_url = None
+    paper_title = None
+    code_url = None
+    code_version = None
+    repo_url = None
 
     with open(md_file, 'r') as md:
         lines = md.readlines()
@@ -70,7 +90,36 @@ def parse_md(md_file):
             if len(line) == 0:
                 i += 1
                 continue
-            if line[:3] == '###':
+            if line[:2] == '# ':
+                paper_title = line.replace('# ', '')
+                i += 1
+            elif line[:3] == '<a ':
+                content = etree.HTML(line)
+                node = content.xpath('//a')[0]
+                if node.text == 'Code Snippet':
+                    code_url = node.get('href', None)
+                    assert code_url is not None, (
+                        f'{collection_name} hasn\'t code snippet url.')
+                    # version extraction
+                    filter_str = r'blob/(.*)/mm'
+                    pattern = re.compile(filter_str)
+                    code_version = pattern.findall(code_url)
+                    assert len(code_version) == 1, (
+                        f'false regular expression ({filter_str}) use.')
+                    code_version = code_version[0]
+                elif node.text == 'Official Repo':
+                    repo_url = node.get('href', None)
+                    assert repo_url is not None, (
+                        f'{collection_name} hasn\'t official repo url.')
+                i += 1
+            elif line[:9] == '<summary ':
+                content = etree.HTML(line)
+                nodes = content.xpath('//a')
+                assert len(nodes) == 1, (
+                    'summary tag should only have single a tag.')
+                paper_url = nodes[0].get('href', None)
+                i += 1
+            elif line[:4] == '### ':
                 datasets.append(line[4:])
                 current_dataset = line[4:]
                 i += 2
@@ -113,22 +162,28 @@ def parse_md(md_file):
                     crop_size = els[crop_size_id].split('x')
                     assert len(crop_size) == 2
                     model = {
-                        'Name': model_name,
-                        'In Collection': collection_name,
+                        'Name':
+                        model_name,
+                        'In Collection':
+                        collection_name,
                         'Metadata': {
                             'backbone': els[backbone_id],
                             'crop size': f'({crop_size[0]},{crop_size[1]})',
                             'lr schd': int(els[lr_schd_id]),
                         },
-                        'Results': {
-                            'Task': 'Semantic Segmentation',
-                            'Dataset': current_dataset,
-                            'Metrics': {
-                                'mIoU': float(els[ss_id]),
+                        'Results': [
+                            {
+                                'Task': 'Semantic Segmentation',
+                                'Dataset': current_dataset,
+                                'Metrics': {
+                                    'mIoU': float(els[ss_id]),
+                                },
                             },
-                        },
-                        'Config': config,
-                        'Weights': weight,
+                        ],
+                        'Config':
+                        config,
+                        'Weights':
+                        weight,
                     }
                     if fps != -1:
                         try:
@@ -152,15 +207,38 @@ def parse_md(md_file):
                         }]
                     if mem != -1:
                         model['Metadata']['memory (GB)'] = float(mem)
+                    # Only have semantic segmentation now
                     if ms_id and els[ms_id] != '-' and els[ms_id] != '':
-                        model['Results']['Metrics']['mIoU(ms+flip)'] = float(
-                            els[ms_id])
+                        model['Results'][0]['Metrics'][
+                            'mIoU(ms+flip)'] = float(els[ms_id])
                     models.append(model)
                     j += 1
                 i = j
             else:
                 i += 1
+    flag = (code_url is not None) and (paper_url is not None) and (repo_url
+                                                                   is not None)
+    assert flag, f'{collection_name} readme error'
     collection['Metadata']['Training Data'] = datasets
+    collection['Code']['URL'] = code_url
+    collection['Code']['Version'] = code_version
+    collection['Paper']['URL'] = paper_url
+    collection['Paper']['Title'] = paper_title
+    collection['Converted From']['Code'] = repo_url
+    # ['Converted From']['Weights] miss
+    # remove empty attribute
+    check_key_list = ['Code', 'Paper', 'Converted From']
+    for check_key in check_key_list:
+        key_list = list(collection[check_key].keys())
+        for key in key_list:
+            if check_key not in collection:
+                break
+            if collection[check_key][key] == '':
+                if len(collection[check_key].keys()) == 1:
+                    collection.pop(check_key)
+                else:
+                    collection[check_key].pop(key)
+
     result = {'Collections': [collection], 'Models': models}
     yml_file = f'{md_file[:-9]}{collection_name}.yml'
     return dump_yaml_and_check_difference(result, yml_file)
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 040af34..fc54a6d 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -45,7 +45,7 @@ repos:
         name: update-model-index
         description: Collect model information and update model-index.yml
         entry: .dev/md2yml.py
-        additional_dependencies: [mmcv]
+        additional_dependencies: [mmcv, lxml]
         language: python
         files: ^configs/.*\.md$
         require_serial: true
diff --git a/README.md b/README.md
index 21c0802..28c904c 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,7 @@ Supported backbones:
 - [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
 - [x] [Vision Transformer (ICLR'2021)](configs/vit)
 - [x] [Swin Transformer (ArXiv'2021)](configs/swin)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
 
 Supported methods:
 
@@ -94,7 +95,7 @@ Supported methods:
 - [x] [PointRend (CVPR'2020)](configs/point_rend)
 - [x] [CGNet (TIP'2020)](configs/cgnet)
 - [x] [SETR (CVPR'2021)](configs/setr)
-- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
+- [x] [DPT (ArXiv'2021)](configs/dpt)
 - [x] [SegFormer (ArXiv'2021)](configs/segformer)
 
 Supported datasets:
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 51e78f7..cc750f6 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -65,6 +65,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
 - [x] [Vision Transformer (ICLR'2021)](configs/vit)
 - [x] [Swin Transformer (ArXiv'2021)](configs/swin)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
 
 已支持的算法：
 
@@ -93,7 +94,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [PointRend (CVPR'2020)](configs/point_rend)
 - [x] [CGNet (TIP'2020)](configs/cgnet)
 - [x] [SETR (CVPR'2021)](configs/setr)
-- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
+- [x] [DPT (ArXiv'2021)](configs/dpt)
 - [x] [SegFormer (ArXiv'2021)](configs/segformer)
 
 已支持的数据集：
diff --git a/configs/ann/README.md b/configs/ann/README.md
index 94b1806..0acf01b 100644
--- a/configs/ann/README.md
+++ b/configs/ann/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/MendelXu/ANN">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1908.07678">ANN (ICCV'2019)</a></summary>
+
 ```latex
 @inproceedings{zhu2019asymmetric,
   title={Asymmetric non-local neural networks for semantic segmentation},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/ann/ann.yml b/configs/ann/ann.yml
index e1fba75..b819c22 100644
--- a/configs/ann/ann.yml
+++ b/configs/ann/ann.yml
@@ -1,296 +1,305 @@
 Collections:
-- Metadata:
+- Name: ann
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: ann
+  Paper:
+    URL: https://arxiv.org/abs/1908.07678
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+  README: configs/ann/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/MendelXu/ANN
 Models:
-- Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
+- Name: ann_r50-d8_512x1024_40k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 269.54
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 269.54
-    lr schd: 40000
     memory (GB): 6.0
-  Name: ann_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.4
       mIoU(ms+flip): 78.57
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
-- Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
+- Name: ann_r101-d8_512x1024_40k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 392.16
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 392.16
-    lr schd: 40000
     memory (GB): 9.5
-  Name: ann_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.55
       mIoU(ms+flip): 78.85
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
-- Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
+- Name: ann_r50-d8_769x769_40k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 588.24
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 588.24
-    lr schd: 40000
     memory (GB): 6.8
-  Name: ann_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.89
       mIoU(ms+flip): 80.46
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
-- Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
+- Name: ann_r101-d8_769x769_40k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 869.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 869.57
-    lr schd: 40000
     memory (GB): 10.7
-  Name: ann_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.32
       mIoU(ms+flip): 80.94
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
-- Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
+- Name: ann_r50-d8_512x1024_80k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: ann_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.34
       mIoU(ms+flip): 78.65
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
-- Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
+- Name: ann_r101-d8_512x1024_80k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: ann_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.14
       mIoU(ms+flip): 78.81
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
-- Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
+- Name: ann_r50-d8_769x769_80k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: ann_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.88
       mIoU(ms+flip): 80.57
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
-- Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
+- Name: ann_r101-d8_769x769_80k_cityscapes
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: ann_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.8
       mIoU(ms+flip): 80.34
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
-- Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
+- Name: ann_r50-d8_512x512_80k_ade20k
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.6
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.6
-    lr schd: 80000
     memory (GB): 9.1
-  Name: ann_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.01
       mIoU(ms+flip): 42.3
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
-- Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
+- Name: ann_r101-d8_512x512_80k_ade20k
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.82
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 70.82
-    lr schd: 80000
     memory (GB): 12.5
-  Name: ann_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.94
       mIoU(ms+flip): 44.18
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
-- Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
+- Name: ann_r50-d8_512x512_160k_ade20k
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: ann_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.74
       mIoU(ms+flip): 42.62
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
-- Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
+- Name: ann_r101-d8_512x512_160k_ade20k
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: ann_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.94
       mIoU(ms+flip): 44.06
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
-- Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
+- Name: ann_r50-d8_512x512_20k_voc12aug
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.8
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.8
-    lr schd: 20000
     memory (GB): 6.0
-  Name: ann_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 74.86
       mIoU(ms+flip): 76.13
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
-- Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
+- Name: ann_r101-d8_512x512_20k_voc12aug
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 71.74
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 71.74
-    lr schd: 20000
     memory (GB): 9.5
-  Name: ann_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.47
       mIoU(ms+flip): 78.7
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
-- Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
+- Name: ann_r50-d8_512x512_40k_voc12aug
   In Collection: ann
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: ann_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.56
       mIoU(ms+flip): 77.51
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
-- Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
+- Name: ann_r101-d8_512x512_40k_voc12aug
   In Collection: ann
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: ann_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.7
       mIoU(ms+flip): 78.06
-    Task: Semantic Segmentation
+  Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
diff --git a/configs/apcnet/README.md b/configs/apcnet/README.md
index b89ac6d..6393a81 100644
--- a/configs/apcnet/README.md
+++ b/configs/apcnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/Junjun2016/APCNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html">APCNet (CVPR'2019)</a></summary>
+
 ```latex
 @InProceedings{He_2019_CVPR,
 author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu},
@@ -14,6 +21,8 @@ year = {2019}
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/apcnet/apcnet.yml b/configs/apcnet/apcnet.yml
index 1ec25ed..32bcfc3 100644
--- a/configs/apcnet/apcnet.yml
+++ b/configs/apcnet/apcnet.yml
@@ -1,223 +1,232 @@
 Collections:
-- Metadata:
+- Name: apcnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: apcnet
+  Paper:
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+  README: configs/apcnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/Junjun2016/APCNet
 Models:
-- Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: apcnet_r50-d8_512x1024_40k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 280.11
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 280.11
-    lr schd: 40000
     memory (GB): 7.7
-  Name: apcnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.02
       mIoU(ms+flip): 79.26
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
-- Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: apcnet_r101-d8_512x1024_40k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 465.12
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 465.12
-    lr schd: 40000
     memory (GB): 11.2
-  Name: apcnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.08
       mIoU(ms+flip): 80.34
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
-- Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
+- Name: apcnet_r50-d8_769x769_40k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 657.89
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 657.89
-    lr schd: 40000
     memory (GB): 8.7
-  Name: apcnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.89
       mIoU(ms+flip): 79.75
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
-- Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
+- Name: apcnet_r101-d8_769x769_40k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 970.87
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 970.87
-    lr schd: 40000
     memory (GB): 12.7
-  Name: apcnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.96
       mIoU(ms+flip): 79.24
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
-- Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: apcnet_r50-d8_512x1024_80k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: apcnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.96
       mIoU(ms+flip): 79.94
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
-- Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: apcnet_r101-d8_512x1024_80k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: apcnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.64
       mIoU(ms+flip): 80.61
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
-- Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
+- Name: apcnet_r50-d8_769x769_80k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: apcnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.79
       mIoU(ms+flip): 80.35
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
-- Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
+- Name: apcnet_r101-d8_769x769_80k_cityscapes
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: apcnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.45
       mIoU(ms+flip): 79.91
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
-- Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
+- Name: apcnet_r50-d8_512x512_80k_ade20k
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 50.99
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 50.99
-    lr schd: 80000
     memory (GB): 10.1
-  Name: apcnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.2
       mIoU(ms+flip): 43.3
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
-- Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
+- Name: apcnet_r101-d8_512x512_80k_ade20k
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 76.34
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 76.34
-    lr schd: 80000
     memory (GB): 13.6
-  Name: apcnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.54
       mIoU(ms+flip): 46.65
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
-- Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
+- Name: apcnet_r50-d8_512x512_160k_ade20k
   In Collection: apcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: apcnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.4
       mIoU(ms+flip): 43.94
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
-- Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py
+- Name: apcnet_r101-d8_512x512_160k_ade20k
   In Collection: apcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: apcnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.41
       mIoU(ms+flip): 46.63
-    Task: Semantic Segmentation
+  Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
diff --git a/configs/bisenetv2/README.md b/configs/bisenetv2/README.md
index 48ecf06..98d96b8 100644
--- a/configs/bisenetv2/README.md
+++ b/configs/bisenetv2/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2004.02147">BiSeNetV2 (IJCV'2021)</a></summary>
+
 ```latex
 @article{yu2021bisenet,
   title={Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation},
@@ -15,6 +22,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/bisenetv2/bisenetv2.yml b/configs/bisenetv2/bisenetv2.yml
index 7c98dd2..373edb9 100644
--- a/configs/bisenetv2/bisenetv2.yml
+++ b/configs/bisenetv2/bisenetv2.yml
@@ -1,80 +1,88 @@
 Collections:
-- Metadata:
+- Name: bisenetv2
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: bisenetv2
+  Paper:
+    URL: https://arxiv.org/abs/2004.02147
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+  README: configs/bisenetv2/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
+    Version: v0.18.0
 Models:
-- Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
+- Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
   In Collection: bisenetv2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 31.48
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (1024,1024)
-      value: 31.48
-    lr schd: 160000
     memory (GB): 7.64
-  Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.21
       mIoU(ms+flip): 75.74
-    Task: Semantic Segmentation
+  Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
-- Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
+- Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
   In Collection: bisenetv2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
     lr schd: 160000
     memory (GB): 7.64
-  Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.57
       mIoU(ms+flip): 75.8
-    Task: Semantic Segmentation
+  Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
-- Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
+- Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
   In Collection: bisenetv2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
     lr schd: 160000
     memory (GB): 15.05
-  Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.76
       mIoU(ms+flip): 77.79
-    Task: Semantic Segmentation
+  Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
-- Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
+- Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
   In Collection: bisenetv2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 27.29
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (1024,1024)
-      value: 27.29
-    lr schd: 160000
     memory (GB): 5.77
-  Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.07
       mIoU(ms+flip): 75.13
-    Task: Semantic Segmentation
+  Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
diff --git a/configs/ccnet/README.md b/configs/ccnet/README.md
index 1c8ba1c..3d2c47f 100644
--- a/configs/ccnet/README.md
+++ b/configs/ccnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/speedinghzl/CCNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1811.11721">CCNet (ICCV'2019)</a></summary>
+
 ```latex
 @article{huang2018ccnet,
     title={CCNet: Criss-Cross Attention for Semantic Segmentation},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/ccnet/ccnet.yml b/configs/ccnet/ccnet.yml
index 85a1c28..d8303ba 100644
--- a/configs/ccnet/ccnet.yml
+++ b/configs/ccnet/ccnet.yml
@@ -1,296 +1,305 @@
 Collections:
-- Metadata:
+- Name: ccnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: ccnet
+  Paper:
+    URL: https://arxiv.org/abs/1811.11721
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+  README: configs/ccnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/speedinghzl/CCNet
 Models:
-- Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: ccnet_r50-d8_512x1024_40k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 301.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 301.2
-    lr schd: 40000
     memory (GB): 6.0
-  Name: ccnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.76
       mIoU(ms+flip): 78.87
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: ccnet_r101-d8_512x1024_40k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 432.9
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 432.9
-    lr schd: 40000
     memory (GB): 9.5
-  Name: ccnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.35
       mIoU(ms+flip): 78.19
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
-- Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
+- Name: ccnet_r50-d8_769x769_40k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 699.3
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 699.3
-    lr schd: 40000
     memory (GB): 6.8
-  Name: ccnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.46
       mIoU(ms+flip): 79.93
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
-- Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
+- Name: ccnet_r101-d8_769x769_40k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 990.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 990.1
-    lr schd: 40000
     memory (GB): 10.7
-  Name: ccnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.94
       mIoU(ms+flip): 78.62
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
-- Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: ccnet_r50-d8_512x1024_80k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: ccnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.03
       mIoU(ms+flip): 80.16
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: ccnet_r101-d8_512x1024_80k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: ccnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.87
       mIoU(ms+flip): 79.9
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
-- Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
+- Name: ccnet_r50-d8_769x769_80k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: ccnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.29
       mIoU(ms+flip): 81.08
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
-- Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
+- Name: ccnet_r101-d8_769x769_80k_cityscapes
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: ccnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.45
       mIoU(ms+flip): 80.66
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
-- Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
+- Name: ccnet_r50-d8_512x512_80k_ade20k
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.87
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.87
-    lr schd: 80000
     memory (GB): 8.8
-  Name: ccnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.78
       mIoU(ms+flip): 42.98
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
+- Name: ccnet_r101-d8_512x512_80k_ade20k
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.87
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 70.87
-    lr schd: 80000
     memory (GB): 12.2
-  Name: ccnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.97
       mIoU(ms+flip): 45.13
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
-- Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
+- Name: ccnet_r50-d8_512x512_160k_ade20k
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: ccnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.08
       mIoU(ms+flip): 43.13
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
+- Name: ccnet_r101-d8_512x512_160k_ade20k
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: ccnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.71
       mIoU(ms+flip): 45.04
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
-- Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
+- Name: ccnet_r50-d8_512x512_20k_voc12aug
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 48.9
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 48.9
-    lr schd: 20000
     memory (GB): 6.0
-  Name: ccnet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.17
       mIoU(ms+flip): 77.51
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
+- Name: ccnet_r101-d8_512x512_20k_voc12aug
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 73.31
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 73.31
-    lr schd: 20000
     memory (GB): 9.5
-  Name: ccnet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.27
       mIoU(ms+flip): 79.02
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
-- Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
+- Name: ccnet_r50-d8_512x512_40k_voc12aug
   In Collection: ccnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: ccnet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 75.96
       mIoU(ms+flip): 77.04
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
-- Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
+- Name: ccnet_r101-d8_512x512_40k_voc12aug
   In Collection: ccnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: ccnet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.87
       mIoU(ms+flip): 78.9
-    Task: Semantic Segmentation
+  Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
diff --git a/configs/cgnet/README.md b/configs/cgnet/README.md
index f1cad20..f7c9b1f 100644
--- a/configs/cgnet/README.md
+++ b/configs/cgnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/wutianyiRosun/CGNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/pdf/1811.08201.pdf">CGNet (TIP'2020)</a></summary>
+
 ```latext
 @article{wu2020cgnet,
   title={Cgnet: A light-weight context guided network for semantic segmentation},
@@ -16,6 +23,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/cgnet/cgnet.yml b/configs/cgnet/cgnet.yml
index c4a41aa..d3169e9 100644
--- a/configs/cgnet/cgnet.yml
+++ b/configs/cgnet/cgnet.yml
@@ -1,50 +1,59 @@
 Collections:
-- Metadata:
+- Name: cgnet
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: cgnet
+  Paper:
+    URL: https://arxiv.org/pdf/1811.08201.pdf
+    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
+  README: configs/cgnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/wutianyiRosun/CGNet
 Models:
-- Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
+- Name: cgnet_680x680_60k_cityscapes
   In Collection: cgnet
   Metadata:
     backbone: M3N21
     crop size: (680,680)
+    lr schd: 60000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 32.78
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (680,680)
-      value: 32.78
-    lr schd: 60000
     memory (GB): 7.5
-  Name: cgnet_680x680_60k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 65.63
       mIoU(ms+flip): 68.04
-    Task: Semantic Segmentation
+  Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
-- Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
+- Name: cgnet_512x1024_60k_cityscapes
   In Collection: cgnet
   Metadata:
     backbone: M3N21
     crop size: (512,1024)
+    lr schd: 60000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 32.11
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 32.11
-    lr schd: 60000
     memory (GB): 8.3
-  Name: cgnet_512x1024_60k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 68.27
       mIoU(ms+flip): 70.33
-    Task: Semantic Segmentation
+  Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
diff --git a/configs/danet/README.md b/configs/danet/README.md
index 655a845..4095203 100644
--- a/configs/danet/README.md
+++ b/configs/danet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/junfu1115/DANet/">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1809.02983">DANet (CVPR'2019)</a></summary>
+
 ```latex
 @article{fu2018dual,
   title={Dual Attention Network for Scene Segmentation},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/danet/danet.yml b/configs/danet/danet.yml
index c7857ae..33bec94 100644
--- a/configs/danet/danet.yml
+++ b/configs/danet/danet.yml
@@ -1,292 +1,301 @@
 Collections:
-- Metadata:
+- Name: danet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: danet
+  Paper:
+    URL: https://arxiv.org/abs/1809.02983
+    Title: Dual Attention Network for Scene Segmentation
+  README: configs/danet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/junfu1115/DANet/
 Models:
-- Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
+- Name: danet_r50-d8_512x1024_40k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 375.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 375.94
-    lr schd: 40000
     memory (GB): 7.4
-  Name: danet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.74
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
-- Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
+- Name: danet_r101-d8_512x1024_40k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 502.51
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 502.51
-    lr schd: 40000
     memory (GB): 10.9
-  Name: danet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.52
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
-- Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
+- Name: danet_r50-d8_769x769_40k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 641.03
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 641.03
-    lr schd: 40000
     memory (GB): 8.8
-  Name: danet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.88
       mIoU(ms+flip): 80.62
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
-- Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
+- Name: danet_r101-d8_769x769_40k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 934.58
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 934.58
-    lr schd: 40000
     memory (GB): 12.8
-  Name: danet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.88
       mIoU(ms+flip): 81.47
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
-- Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
+- Name: danet_r50-d8_512x1024_80k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: danet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.34
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
-- Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
+- Name: danet_r101-d8_512x1024_80k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: danet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.41
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
-- Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
+- Name: danet_r50-d8_769x769_80k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: danet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.27
       mIoU(ms+flip): 80.96
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
-- Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
+- Name: danet_r101-d8_769x769_80k_cityscapes
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: danet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.47
       mIoU(ms+flip): 82.02
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
-- Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
+- Name: danet_r50-d8_512x512_80k_ade20k
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.17
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.17
-    lr schd: 80000
     memory (GB): 11.5
-  Name: danet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.66
       mIoU(ms+flip): 42.9
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
-- Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
+- Name: danet_r101-d8_512x512_80k_ade20k
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.52
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 70.52
-    lr schd: 80000
     memory (GB): 15.0
-  Name: danet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.64
       mIoU(ms+flip): 45.19
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
-- Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
+- Name: danet_r50-d8_512x512_160k_ade20k
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: danet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.45
       mIoU(ms+flip): 43.25
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
-- Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
+- Name: danet_r101-d8_512x512_160k_ade20k
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: danet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.17
       mIoU(ms+flip): 45.02
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
-- Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
+- Name: danet_r50-d8_512x512_20k_voc12aug
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.76
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.76
-    lr schd: 20000
     memory (GB): 6.5
-  Name: danet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 74.45
       mIoU(ms+flip): 75.69
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
-- Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
+- Name: danet_r101-d8_512x512_20k_voc12aug
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 72.67
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 72.67
-    lr schd: 20000
     memory (GB): 9.9
-  Name: danet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.02
       mIoU(ms+flip): 77.23
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
-- Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
+- Name: danet_r50-d8_512x512_40k_voc12aug
   In Collection: danet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: danet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.37
       mIoU(ms+flip): 77.29
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
-- Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
+- Name: danet_r101-d8_512x512_40k_voc12aug
   In Collection: danet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: danet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.51
       mIoU(ms+flip): 77.32
-    Task: Semantic Segmentation
+  Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
diff --git a/configs/deeplabv3/README.md b/configs/deeplabv3/README.md
index b40fe0d..28bdbb9 100644
--- a/configs/deeplabv3/README.md
+++ b/configs/deeplabv3/README.md
@@ -4,6 +4,13 @@
 
 <!-- [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/decode_heads/aspp_head.py#L54">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1706.05587">DeepLabV3 (ArXiv'2017)</a></summary>
+
 ```latext
 @article{chen2017rethinking,
   title={Rethinking atrous convolution for semantic image segmentation},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 :::{note}
diff --git a/configs/deeplabv3/deeplabv3.yml b/configs/deeplabv3/deeplabv3.yml
index 1d2b7ca..94acb59 100644
--- a/configs/deeplabv3/deeplabv3.yml
+++ b/configs/deeplabv3/deeplabv3.yml
@@ -1,5 +1,6 @@
 Collections:
-- Metadata:
+- Name: deeplabv3
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
@@ -8,719 +9,727 @@ Collections:
     - Pascal Context 59
     - COCO-Stuff 10k
     - COCO-Stuff 164k
-  Name: deeplabv3
+  Paper:
+    URL: https://arxiv.org/abs/1706.05587
+    Title: Rethinking atrous convolution for semantic image segmentation
+  README: configs/deeplabv3/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
+- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 389.11
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 389.11
-    lr schd: 40000
     memory (GB): 6.1
-  Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.09
       mIoU(ms+flip): 80.45
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
+- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 520.83
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 520.83
-    lr schd: 40000
     memory (GB): 9.6
-  Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.12
       mIoU(ms+flip): 79.61
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
+- Name: deeplabv3_r50-d8_769x769_40k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 900.9
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 900.9
-    lr schd: 40000
     memory (GB): 6.9
-  Name: deeplabv3_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.58
       mIoU(ms+flip): 79.89
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
+- Name: deeplabv3_r101-d8_769x769_40k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 1204.82
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 1204.82
-    lr schd: 40000
     memory (GB): 10.9
-  Name: deeplabv3_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.27
       mIoU(ms+flip): 80.11
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
-- Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 72.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 72.57
-    lr schd: 80000
     memory (GB): 1.7
-  Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.7
       mIoU(ms+flip): 78.27
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.32
       mIoU(ms+flip): 80.57
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.2
       mIoU(ms+flip): 81.21
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
-- Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r18-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 180.18
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 180.18
-    lr schd: 80000
     memory (GB): 1.9
-  Name: deeplabv3_r18-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.6
       mIoU(ms+flip): 78.26
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: deeplabv3_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.89
       mIoU(ms+flip): 81.06
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: deeplabv3_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.67
       mIoU(ms+flip): 80.81
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
     lr schd: 80000
-  Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.36
       mIoU(ms+flip): 79.84
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
-- Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 71.79
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 71.79
-    lr schd: 80000
     memory (GB): 1.6
-  Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.26
       mIoU(ms+flip): 77.88
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
-- Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 364.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 364.96
-    lr schd: 80000
     memory (GB): 6.0
-  Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.63
       mIoU(ms+flip): 80.98
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
-- Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 552.49
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 552.49
-    lr schd: 80000
     memory (GB): 9.5
-  Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.01
       mIoU(ms+flip): 81.21
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
-- Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 172.71
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 172.71
-    lr schd: 80000
     memory (GB): 1.8
-  Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.63
       mIoU(ms+flip): 77.51
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
-- Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 862.07
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 862.07
-    lr schd: 80000
     memory (GB): 6.8
-  Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.8
       mIoU(ms+flip): 80.27
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
-- Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 1219.51
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 1219.51
-    lr schd: 80000
     memory (GB): 10.7
-  Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.41
       mIoU(ms+flip): 80.73
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
+- Name: deeplabv3_r50-d8_512x512_80k_ade20k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.75
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.75
-    lr schd: 80000
     memory (GB): 8.9
-  Name: deeplabv3_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.42
       mIoU(ms+flip): 43.28
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
+- Name: deeplabv3_r101-d8_512x512_80k_ade20k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 98.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 98.62
-    lr schd: 80000
     memory (GB): 12.4
-  Name: deeplabv3_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.08
       mIoU(ms+flip): 45.19
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
+- Name: deeplabv3_r50-d8_512x512_160k_ade20k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.66
       mIoU(ms+flip): 44.09
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
+- Name: deeplabv3_r101-d8_512x512_160k_ade20k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.0
       mIoU(ms+flip): 46.66
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
+- Name: deeplabv3_r50-d8_512x512_20k_voc12aug
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 72.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 72.05
-    lr schd: 20000
     memory (GB): 6.1
-  Name: deeplabv3_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.17
       mIoU(ms+flip): 77.42
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
+- Name: deeplabv3_r101-d8_512x512_20k_voc12aug
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 101.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 101.94
-    lr schd: 20000
     memory (GB): 9.6
-  Name: deeplabv3_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.7
       mIoU(ms+flip): 79.95
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
+- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.68
       mIoU(ms+flip): 78.78
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
+- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.92
       mIoU(ms+flip): 79.18
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
+- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 141.04
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (480,480)
-      value: 141.04
-    lr schd: 40000
     memory (GB): 9.2
-  Name: deeplabv3_r101-d8_480x480_40k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 46.55
       mIoU(ms+flip): 47.81
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
+- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: deeplabv3_r101-d8_480x480_80k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 46.42
       mIoU(ms+flip): 47.53
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 40000
-  Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 52.61
       mIoU(ms+flip): 54.28
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
+- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 52.46
       mIoU(ms+flip): 54.09
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py
+- Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 92.59
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 92.59
-    lr schd: 20000
     memory (GB): 9.6
-  Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 34.66
       mIoU(ms+flip): 36.08
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py
+- Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 114.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 114.94
-    lr schd: 20000
     memory (GB): 13.2
-  Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 37.3
       mIoU(ms+flip): 38.42
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py
+- Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 35.73
       mIoU(ms+flip): 37.09
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py
+- Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 37.81
       mIoU(ms+flip): 38.8
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py
+- Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 92.59
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 92.59
-    lr schd: 80000
     memory (GB): 9.6
-  Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 39.38
       mIoU(ms+flip): 40.03
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py
+- Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 114.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 114.94
-    lr schd: 80000
     memory (GB): 13.2
-  Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 40.87
       mIoU(ms+flip): 41.5
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py
+- Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 41.09
       mIoU(ms+flip): 41.69
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py
+- Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 41.82
       mIoU(ms+flip): 42.49
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth
-- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py
+- Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 320000
-  Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 41.37
       mIoU(ms+flip): 42.22
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth
-- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py
+- Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k
   In Collection: deeplabv3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 320000
-  Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 42.61
       mIoU(ms+flip): 43.42
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth
diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md
index 16702fe..bf6c5d5 100644
--- a/configs/deeplabv3plus/README.md
+++ b/configs/deeplabv3plus/README.md
@@ -4,6 +4,13 @@
 
 <!-- [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/decode_heads/sep_aspp_head.py#L30">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1802.02611">DeepLabV3+ (CVPR'2018)</a></summary>
+
 ```latex
 @inproceedings{deeplabv3plus2018,
   title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 :::{note}
diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml
index d681d30..ff78da3 100644
--- a/configs/deeplabv3plus/deeplabv3plus.yml
+++ b/configs/deeplabv3plus/deeplabv3plus.yml
@@ -1,574 +1,580 @@
 Collections:
-- Metadata:
+- Name: deeplabv3plus
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-    - ' Pascal VOC 2012 + Aug'
-    - ' Pascal Context'
-    - ' Pascal Context 59'
-  Name: deeplabv3plus
+  Paper:
+    URL: https://arxiv.org/abs/1802.02611
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+  README: configs/deeplabv3plus/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
+- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 253.81
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 253.81
-    lr schd: 40000
     memory (GB): 7.5
-  Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.61
       mIoU(ms+flip): 81.01
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
+- Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 384.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 384.62
-    lr schd: 40000
     memory (GB): 11.0
-  Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.21
       mIoU(ms+flip): 81.82
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
+- Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 581.4
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 581.4
-    lr schd: 40000
     memory (GB): 8.5
-  Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.97
       mIoU(ms+flip): 80.46
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
+- Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 869.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 869.57
-    lr schd: 40000
     memory (GB): 12.5
-  Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.46
       mIoU(ms+flip): 80.5
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.08
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 70.08
-    lr schd: 80000
     memory (GB): 2.2
-  Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.89
       mIoU(ms+flip): 78.76
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.09
       mIoU(ms+flip): 81.13
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.97
       mIoU(ms+flip): 82.03
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 174.22
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 174.22
-    lr schd: 80000
     memory (GB): 2.5
-  Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.26
       mIoU(ms+flip): 77.91
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.83
       mIoU(ms+flip): 81.48
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.98
       mIoU(ms+flip): 82.18
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 133.69
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 133.69
-    lr schd: 40000
     memory (GB): 5.8
-  Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.09
       mIoU(ms+flip): 80.36
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
     lr schd: 80000
     memory (GB): 9.9
-  Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.9
       mIoU(ms+flip): 81.33
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 66.89
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 66.89
-    lr schd: 80000
     memory (GB): 2.1
-  Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.87
       mIoU(ms+flip): 77.52
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 253.81
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 253.81
-    lr schd: 80000
     memory (GB): 7.4
-  Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.28
       mIoU(ms+flip): 81.44
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 384.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 384.62
-    lr schd: 80000
     memory (GB): 10.9
-  Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.16
       mIoU(ms+flip): 81.41
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 167.79
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 167.79
-    lr schd: 80000
     memory (GB): 2.4
-  Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.36
       mIoU(ms+flip): 78.24
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 581.4
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 581.4
-    lr schd: 80000
     memory (GB): 8.4
-  Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.41
       mIoU(ms+flip): 80.56
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
+- Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 909.09
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 909.09
-    lr schd: 80000
     memory (GB): 12.3
-  Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.88
       mIoU(ms+flip): 81.46
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
+- Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.6
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.6
-    lr schd: 80000
     memory (GB): 10.6
-  Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.72
       mIoU(ms+flip): 43.75
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
+- Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 70.62
-    lr schd: 80000
     memory (GB): 14.1
-  Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.6
       mIoU(ms+flip): 46.06
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
+- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.95
       mIoU(ms+flip): 44.93
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
+- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.47
       mIoU(ms+flip): 46.35
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
+- Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.62
-    lr schd: 20000
     memory (GB): 7.6
-  Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
   Results:
-    Dataset: ' Pascal VOC 2012 + Aug'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 75.93
       mIoU(ms+flip): 77.5
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
+- Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 72.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 72.05
-    lr schd: 20000
     memory (GB): 11.0
-  Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
   Results:
-    Dataset: ' Pascal VOC 2012 + Aug'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 77.22
       mIoU(ms+flip): 78.59
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
+- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
   Results:
-    Dataset: ' Pascal VOC 2012 + Aug'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 76.81
       mIoU(ms+flip): 77.57
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
+- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
   Results:
-    Dataset: ' Pascal VOC 2012 + Aug'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 78.62
       mIoU(ms+flip): 79.53
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 110.01
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (480,480)
-      value: 110.01
-    lr schd: 40000
-  Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
   Results:
-    Dataset: ' Pascal Context'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 47.3
       mIoU(ms+flip): 48.47
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
   Results:
-    Dataset: ' Pascal Context'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 47.23
       mIoU(ms+flip): 48.26
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 40000
-  Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
   Results:
-    Dataset: ' Pascal Context 59'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 52.86
       mIoU(ms+flip): 54.54
-    Task: Semantic Segmentation
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
   Weights: 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
-- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
   In Collection: deeplabv3plus
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
   Results:
-    Dataset: ' Pascal Context 59'
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
     Metrics:
       mIoU: 53.2
       mIoU(ms+flip): 54.67
-    Task: Semantic Segmentation
+  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
diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md
index 0cea6bf..3eef9cb 100644
--- a/configs/dmnet/README.md
+++ b/configs/dmnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/Junjun2016/DMNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf">DMNet (ICCV'2019)</a></summary>
+
 ```latex
 @InProceedings{He_2019_ICCV,
 author = {He, Junjun and Deng, Zhongying and Qiao, Yu},
@@ -14,6 +21,8 @@ year = {2019}
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/dmnet/dmnet.yml b/configs/dmnet/dmnet.yml
index 3c179b5..f45a659 100644
--- a/configs/dmnet/dmnet.yml
+++ b/configs/dmnet/dmnet.yml
@@ -1,223 +1,232 @@
 Collections:
-- Metadata:
+- Name: dmnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: dmnet
+  Paper:
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+  README: configs/dmnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/Junjun2016/DMNet
 Models:
-- Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: dmnet_r50-d8_512x1024_40k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 273.22
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 273.22
-    lr schd: 40000
     memory (GB): 7.0
-  Name: dmnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.78
       mIoU(ms+flip): 79.14
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
-- Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: dmnet_r101-d8_512x1024_40k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 393.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 393.7
-    lr schd: 40000
     memory (GB): 10.6
-  Name: dmnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.37
       mIoU(ms+flip): 79.72
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
-- Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
+- Name: dmnet_r50-d8_769x769_40k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 636.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 636.94
-    lr schd: 40000
     memory (GB): 7.9
-  Name: dmnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.49
       mIoU(ms+flip): 80.27
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
-- Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
+- Name: dmnet_r101-d8_769x769_40k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 990.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 990.1
-    lr schd: 40000
     memory (GB): 12.0
-  Name: dmnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.62
       mIoU(ms+flip): 78.94
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
-- Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: dmnet_r50-d8_512x1024_80k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: dmnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.07
       mIoU(ms+flip): 80.22
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
-- Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: dmnet_r101-d8_512x1024_80k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: dmnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.64
       mIoU(ms+flip): 80.67
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
-- Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
+- Name: dmnet_r50-d8_769x769_80k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: dmnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.22
       mIoU(ms+flip): 80.55
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
-- Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
+- Name: dmnet_r101-d8_769x769_80k_cityscapes
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: dmnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.19
       mIoU(ms+flip): 80.65
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
-- Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
+- Name: dmnet_r50-d8_512x512_80k_ade20k
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.73
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.73
-    lr schd: 80000
     memory (GB): 9.4
-  Name: dmnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.37
       mIoU(ms+flip): 43.62
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
-- Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
+- Name: dmnet_r101-d8_512x512_80k_ade20k
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 72.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 72.05
-    lr schd: 80000
     memory (GB): 13.0
-  Name: dmnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.34
       mIoU(ms+flip): 46.13
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
-- Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
+- Name: dmnet_r50-d8_512x512_160k_ade20k
   In Collection: dmnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: dmnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.15
       mIoU(ms+flip): 44.17
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
-- Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
+- Name: dmnet_r101-d8_512x512_160k_ade20k
   In Collection: dmnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: dmnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.42
       mIoU(ms+flip): 46.76
-    Task: Semantic Segmentation
+  Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
diff --git a/configs/dnlnet/README.md b/configs/dnlnet/README.md
index 7371412..3bf4b21 100644
--- a/configs/dnlnet/README.md
+++ b/configs/dnlnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/yinmh17/DNL-Semantic-Segmentation">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2006.06668">DNLNet (ECCV'2020)</a></summary>
+
 This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://arxiv.org/abs/2006.06668) for semantic segmentation. It is still in progress.
 
 ## Citation
@@ -17,6 +24,8 @@ This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://
 }
 ```
 
+</details>
+
 ## Results and models (in progress)
 
 ### Cityscapes
diff --git a/configs/dnlnet/dnlnet.yml b/configs/dnlnet/dnlnet.yml
index 03de1c7..79dee30 100644
--- a/configs/dnlnet/dnlnet.yml
+++ b/configs/dnlnet/dnlnet.yml
@@ -1,219 +1,228 @@
 Collections:
-- Metadata:
+- Name: dnlnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: dnlnet
+  Paper:
+    URL: https://arxiv.org/abs/2006.06668
+    Title: Disentangled Non-Local Neural Networks
+  README: configs/dnlnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/yinmh17/DNL-Semantic-Segmentation
 Models:
-- Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
+- Name: dnl_r50-d8_512x1024_40k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 390.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 390.62
-    lr schd: 40000
     memory (GB): 7.3
-  Name: dnl_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.61
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
-- Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
+- Name: dnl_r101-d8_512x1024_40k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 510.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 510.2
-    lr schd: 40000
     memory (GB): 10.9
-  Name: dnl_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.31
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
-- Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
+- Name: dnl_r50-d8_769x769_40k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 666.67
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 666.67
-    lr schd: 40000
     memory (GB): 9.2
-  Name: dnl_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.44
       mIoU(ms+flip): 80.27
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
-- Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
+- Name: dnl_r101-d8_769x769_40k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 980.39
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 980.39
-    lr schd: 40000
     memory (GB): 12.6
-  Name: dnl_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.39
       mIoU(ms+flip): 77.77
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
-- Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
+- Name: dnl_r50-d8_512x1024_80k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: dnl_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.33
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
-- Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
+- Name: dnl_r101-d8_512x1024_80k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: dnl_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.41
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
-- Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
+- Name: dnl_r50-d8_769x769_80k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: dnl_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.36
       mIoU(ms+flip): 80.7
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
-- Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
+- Name: dnl_r101-d8_769x769_80k_cityscapes
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: dnl_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.41
       mIoU(ms+flip): 80.68
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
-- Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
+- Name: dnl_r50-d8_512x512_80k_ade20k
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 48.4
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 48.4
-    lr schd: 80000
     memory (GB): 8.8
-  Name: dnl_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.76
       mIoU(ms+flip): 42.99
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
-- Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
+- Name: dnl_r101-d8_512x512_80k_ade20k
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 79.74
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 79.74
-    lr schd: 80000
     memory (GB): 12.8
-  Name: dnl_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.76
       mIoU(ms+flip): 44.91
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
-- Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
+- Name: dnl_r50-d8_512x512_160k_ade20k
   In Collection: dnlnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: dnl_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.87
       mIoU(ms+flip): 43.01
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
-- Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
+- Name: dnl_r101-d8_512x512_160k_ade20k
   In Collection: dnlnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: dnl_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.25
       mIoU(ms+flip): 45.78
-    Task: Semantic Segmentation
+  Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
diff --git a/configs/dpt/README.md b/configs/dpt/README.md
index 3dd994c..c57320c 100644
--- a/configs/dpt/README.md
+++ b/configs/dpt/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/isl-org/DPT">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2103.13413">DPT (ArXiv'2021)</a></summary>
+
 ```latex
 @article{dosoViTskiy2020,
   title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
@@ -20,6 +27,8 @@
 }
 ```
 
+</details>
+
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
diff --git a/configs/dpt/dpt.yml b/configs/dpt/dpt.yml
index affb8d4..9e59b35 100644
--- a/configs/dpt/dpt.yml
+++ b/configs/dpt/dpt.yml
@@ -1,28 +1,37 @@
 Collections:
-- Metadata:
+- Name: dpt
+  Metadata:
     Training Data:
     - ADE20K
-  Name: dpt
+  Paper:
+    URL: https://arxiv.org/abs/2103.13413
+    Title: Vision Transformer for Dense Prediction
+  README: configs/dpt/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/isl-org/DPT
 Models:
-- Config: configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py
+- Name: dpt_vit-b16_512x512_160k_ade20k
   In Collection: dpt
   Metadata:
     backbone: ViT-B
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 96.06
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 96.06
-    lr schd: 160000
     memory (GB): 8.09
-  Name: dpt_vit-b16_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 46.97
       mIoU(ms+flip): 48.34
-    Task: Semantic Segmentation
+  Config: configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth
diff --git a/configs/emanet/README.md b/configs/emanet/README.md
index ec2d726..0bfedcb 100644
--- a/configs/emanet/README.md
+++ b/configs/emanet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://xialipku.github.io/EMANet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1907.13426">EMANet (ICCV'2019)</a></summary>
+
 ```latex
 @inproceedings{li2019expectation,
   title={Expectation-maximization attention networks for semantic segmentation},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/emanet/emanet.yml b/configs/emanet/emanet.yml
index be2d377..55ea80c 100644
--- a/configs/emanet/emanet.yml
+++ b/configs/emanet/emanet.yml
@@ -1,94 +1,103 @@
 Collections:
-- Metadata:
+- Name: emanet
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: emanet
+  Paper:
+    URL: https://arxiv.org/abs/1907.13426
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+  README: configs/emanet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
+    Version: v0.17.0
+  Converted From:
+    Code: https://xialipku.github.io/EMANet
 Models:
-- Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
+- Name: emanet_r50-d8_512x1024_80k_cityscapes
   In Collection: emanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 218.34
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 218.34
-    lr schd: 80000
     memory (GB): 5.4
-  Name: emanet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.59
       mIoU(ms+flip): 79.44
-    Task: Semantic Segmentation
+  Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
-- Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
+- Name: emanet_r101-d8_512x1024_80k_cityscapes
   In Collection: emanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 348.43
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 348.43
-    lr schd: 80000
     memory (GB): 6.2
-  Name: emanet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.1
       mIoU(ms+flip): 81.21
-    Task: Semantic Segmentation
+  Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
-- Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
+- Name: emanet_r50-d8_769x769_80k_cityscapes
   In Collection: emanet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 507.61
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 507.61
-    lr schd: 80000
     memory (GB): 8.9
-  Name: emanet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.33
       mIoU(ms+flip): 80.49
-    Task: Semantic Segmentation
+  Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
-- Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
+- Name: emanet_r101-d8_769x769_80k_cityscapes
   In Collection: emanet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 819.67
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 819.67
-    lr schd: 80000
     memory (GB): 10.1
-  Name: emanet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.62
       mIoU(ms+flip): 81.0
-    Task: Semantic Segmentation
+  Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
diff --git a/configs/encnet/README.md b/configs/encnet/README.md
index 4246caa..26b63dc 100644
--- a/configs/encnet/README.md
+++ b/configs/encnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/zhanghang1989/PyTorch-Encoding">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1803.08904">EncNet (CVPR'2018)</a></summary>
+
 ```latex
 @InProceedings{Zhang_2018_CVPR,
 author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
@@ -14,6 +21,8 @@ year = {2018}
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/encnet/encnet.yml b/configs/encnet/encnet.yml
index bbde966..24e0ab1 100644
--- a/configs/encnet/encnet.yml
+++ b/configs/encnet/encnet.yml
@@ -1,223 +1,232 @@
 Collections:
-- Metadata:
+- Name: encnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: encnet
+  Paper:
+    URL: https://arxiv.org/abs/1803.08904
+    Title: Context Encoding for Semantic Segmentation
+  README: configs/encnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/zhanghang1989/PyTorch-Encoding
 Models:
-- Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: encnet_r50-d8_512x1024_40k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 218.34
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 218.34
-    lr schd: 40000
     memory (GB): 8.6
-  Name: encnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.67
       mIoU(ms+flip): 77.08
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
-- Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: encnet_r101-d8_512x1024_40k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 375.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 375.94
-    lr schd: 40000
     memory (GB): 12.1
-  Name: encnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.81
       mIoU(ms+flip): 77.21
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
-- Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
+- Name: encnet_r50-d8_769x769_40k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 549.45
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 549.45
-    lr schd: 40000
     memory (GB): 9.8
-  Name: encnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.24
       mIoU(ms+flip): 77.85
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
-- Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
+- Name: encnet_r101-d8_769x769_40k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 793.65
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 793.65
-    lr schd: 40000
     memory (GB): 13.7
-  Name: encnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 74.25
       mIoU(ms+flip): 76.25
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
-- Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: encnet_r50-d8_512x1024_80k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: encnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.94
       mIoU(ms+flip): 79.13
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
-- Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: encnet_r101-d8_512x1024_80k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: encnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.55
       mIoU(ms+flip): 79.47
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
-- Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
+- Name: encnet_r50-d8_769x769_80k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: encnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.44
       mIoU(ms+flip): 78.72
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
-- Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
+- Name: encnet_r101-d8_769x769_80k_cityscapes
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: encnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.1
       mIoU(ms+flip): 76.97
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
-- Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
+- Name: encnet_r50-d8_512x512_80k_ade20k
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 43.84
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 43.84
-    lr schd: 80000
     memory (GB): 10.1
-  Name: encnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 39.53
       mIoU(ms+flip): 41.17
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
-- Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
+- Name: encnet_r101-d8_512x512_80k_ade20k
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.25
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.25
-    lr schd: 80000
     memory (GB): 13.6
-  Name: encnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.11
       mIoU(ms+flip): 43.61
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
-- Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
+- Name: encnet_r50-d8_512x512_160k_ade20k
   In Collection: encnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: encnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 40.1
       mIoU(ms+flip): 41.71
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
-- Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
+- Name: encnet_r101-d8_512x512_160k_ade20k
   In Collection: encnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: encnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.61
       mIoU(ms+flip): 44.01
-    Task: Semantic Segmentation
+  Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
diff --git a/configs/fastscnn/README.md b/configs/fastscnn/README.md
index 5b403b6..1801d5f 100644
--- a/configs/fastscnn/README.md
+++ b/configs/fastscnn/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1902.04502">Fast-SCNN (ArXiv'2019)</a></summary>
+
 ```latex
 @article{poudel2019fast,
   title={Fast-scnn: Fast semantic segmentation network},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/fastscnn/fastscnn.yml b/configs/fastscnn/fastscnn.yml
index 00e7f21..287c6c8 100644
--- a/configs/fastscnn/fastscnn.yml
+++ b/configs/fastscnn/fastscnn.yml
@@ -1,28 +1,35 @@
 Collections:
-- Metadata:
+- Name: fastscnn
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: fastscnn
+  Paper:
+    URL: https://arxiv.org/abs/1902.04502
+    Title: Fast-SCNN for Semantic Segmentation
+  README: configs/fastscnn/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272
+    Version: v0.17.0
 Models:
-- Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
+- Name: fast_scnn_lr0.12_8x4_160k_cityscapes
   In Collection: fastscnn
   Metadata:
     backbone: Fast-SCNN
     crop size: (512,1024)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 17.71
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 17.71
-    lr schd: 160000
     memory (GB): 3.3
-  Name: fast_scnn_lr0.12_8x4_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 70.96
       mIoU(ms+flip): 72.65
-    Task: Semantic Segmentation
+  Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth
diff --git a/configs/fcn/README.md b/configs/fcn/README.md
index 396652c..d33f402 100644
--- a/configs/fcn/README.md
+++ b/configs/fcn/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1411.4038">FCN (CVPR'2015/TPAMI'2017)</a></summary>
+
 ```latex
 @article{shelhamer2017fully,
   title={Fully convolutional networks for semantic segmentation},
@@ -17,6 +24,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/fcn/fcn.yml b/configs/fcn/fcn.yml
index 965794d..3f889c4 100644
--- a/configs/fcn/fcn.yml
+++ b/configs/fcn/fcn.yml
@@ -1,797 +1,806 @@
 Collections:
-- Metadata:
+- Name: fcn
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
     - Pascal Context
     - Pascal Context 59
-  Name: fcn
+  Paper:
+    URL: https://arxiv.org/abs/1411.4038
+    Title: Fully Convolutional Networks for Semantic Segmentation
+  README: configs/fcn/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn
 Models:
-- Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
+- Name: fcn_r50-d8_512x1024_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 239.81
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 239.81
-    lr schd: 40000
     memory (GB): 5.7
-  Name: fcn_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 72.25
       mIoU(ms+flip): 73.36
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
-- Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
+- Name: fcn_r101-d8_512x1024_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 375.94
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 375.94
-    lr schd: 40000
     memory (GB): 9.2
-  Name: fcn_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.45
       mIoU(ms+flip): 76.58
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
-- Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
+- Name: fcn_r50-d8_769x769_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 555.56
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 555.56
-    lr schd: 40000
     memory (GB): 6.5
-  Name: fcn_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 71.47
       mIoU(ms+flip): 72.54
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
-- Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
+- Name: fcn_r101-d8_769x769_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 840.34
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 840.34
-    lr schd: 40000
     memory (GB): 10.4
-  Name: fcn_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.93
       mIoU(ms+flip): 75.14
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
-- Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r18-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 68.26
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 68.26
-    lr schd: 80000
     memory (GB): 1.7
-  Name: fcn_r18-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 71.11
       mIoU(ms+flip): 72.91
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
-- Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r50-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: fcn_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.61
       mIoU(ms+flip): 74.24
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
-- Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r101-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: fcn_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.13
       mIoU(ms+flip): 75.94
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
-- Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
+- Name: fcn_r18-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 156.25
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 156.25
-    lr schd: 80000
     memory (GB): 1.9
-  Name: fcn_r18-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 70.8
       mIoU(ms+flip): 73.16
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
-- Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
+- Name: fcn_r50-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: fcn_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 72.64
       mIoU(ms+flip): 73.32
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
-- Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
+- Name: fcn_r101-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: fcn_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.52
       mIoU(ms+flip): 76.61
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
-- Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r18b-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 59.74
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 59.74
-    lr schd: 80000
     memory (GB): 1.6
-  Name: fcn_r18b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 70.24
       mIoU(ms+flip): 72.77
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
-- Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r50b-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 238.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 238.1
-    lr schd: 80000
     memory (GB): 5.6
-  Name: fcn_r50b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.65
       mIoU(ms+flip): 77.59
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
-- Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
+- Name: fcn_r101b-d8_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 366.3
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 366.3
-    lr schd: 80000
     memory (GB): 9.1
-  Name: fcn_r101b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.37
       mIoU(ms+flip): 78.77
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
-- Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
+- Name: fcn_r18b-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 149.25
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 149.25
-    lr schd: 80000
     memory (GB): 1.7
-  Name: fcn_r18b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 69.66
       mIoU(ms+flip): 72.07
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
-- Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
+- Name: fcn_r50b-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 549.45
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 549.45
-    lr schd: 80000
     memory (GB): 6.3
-  Name: fcn_r50b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.83
       mIoU(ms+flip): 76.6
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
-- Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
+- Name: fcn_r101b-d8_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 869.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 869.57
-    lr schd: 80000
     memory (GB): 10.3
-  Name: fcn_r101b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.02
       mIoU(ms+flip): 78.67
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
-- Config: configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
+- Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D16
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 97.85
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 97.85
-    lr schd: 40000
     memory (GB): 3.4
-  Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.06
       mIoU(ms+flip): 78.85
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth
-- Config: configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
+- Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D16
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 96.62
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 96.62
-    lr schd: 80000
-  Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.27
       mIoU(ms+flip): 78.88
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth
-- Config: configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py
+- Name: fcn_d6_r50-d16_769x769_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D16
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 239.81
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 239.81
-    lr schd: 40000
     memory (GB): 3.7
-  Name: fcn_d6_r50-d16_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.82
       mIoU(ms+flip): 78.22
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth
-- Config: configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py
+- Name: fcn_d6_r50-d16_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50-D16
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 240.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 240.96
-    lr schd: 80000
-  Name: fcn_d6_r50-d16_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.04
       mIoU(ms+flip): 78.4
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth
-- Config: configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
+- Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D16
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 124.38
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 124.38
-    lr schd: 40000
     memory (GB): 4.5
-  Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.36
       mIoU(ms+flip): 79.18
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth
-- Config: configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
+- Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D16
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 121.07
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 121.07
-    lr schd: 80000
-  Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.46
       mIoU(ms+flip): 80.42
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth
-- Config: configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py
+- Name: fcn_d6_r101-d16_769x769_40k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D16
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 320.51
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 320.51
-    lr schd: 40000
     memory (GB): 5.0
-  Name: fcn_d6_r101-d16_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.28
       mIoU(ms+flip): 78.95
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth
-- Config: configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py
+- Name: fcn_d6_r101-d16_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101-D16
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 311.53
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 311.53
-    lr schd: 80000
-  Name: fcn_d6_r101-d16_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.06
       mIoU(ms+flip): 79.58
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth
-- Config: configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py
+- Name: fcn_d6_r50b-d16_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50b-D16
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 98.43
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 98.43
-    lr schd: 80000
     memory (GB): 3.2
-  Name: fcn_d6_r50b-d16_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.99
       mIoU(ms+flip): 79.03
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth
-- Config: configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
+- Name: fcn_d6_r50b-d16_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-50b-D16
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 239.81
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 239.81
-    lr schd: 80000
     memory (GB): 3.6
-  Name: fcn_d6_r50b-d16_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.86
       mIoU(ms+flip): 78.52
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth
-- Config: configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py
+- Name: fcn_d6_r101b-d16_512x1024_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101b-D16
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 118.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 118.2
-    lr schd: 80000
     memory (GB): 4.3
-  Name: fcn_d6_r101b-d16_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.72
       mIoU(ms+flip): 79.53
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth
-- Config: configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py
+- Name: fcn_d6_r101b-d16_769x769_80k_cityscapes
   In Collection: fcn
   Metadata:
     backbone: R-101b-D16
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 301.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 301.2
-    lr schd: 80000
     memory (GB): 4.8
-  Name: fcn_d6_r101b-d16_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.34
       mIoU(ms+flip): 78.91
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth
-- Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
+- Name: fcn_r50-d8_512x512_80k_ade20k
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.57
-    lr schd: 80000
     memory (GB): 8.5
-  Name: fcn_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 35.94
       mIoU(ms+flip): 37.94
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
-- Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
+- Name: fcn_r101-d8_512x512_80k_ade20k
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.66
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.66
-    lr schd: 80000
     memory (GB): 12.0
-  Name: fcn_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 39.61
       mIoU(ms+flip): 40.83
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
-- Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
+- Name: fcn_r50-d8_512x512_160k_ade20k
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: fcn_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 36.1
       mIoU(ms+flip): 38.08
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
-- Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
+- Name: fcn_r101-d8_512x512_160k_ade20k
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: fcn_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 39.91
       mIoU(ms+flip): 41.4
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
-- Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
+- Name: fcn_r50-d8_512x512_20k_voc12aug
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.96
-    lr schd: 20000
     memory (GB): 5.7
-  Name: fcn_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 67.08
       mIoU(ms+flip): 69.94
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
-- Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
+- Name: fcn_r101-d8_512x512_20k_voc12aug
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.52
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.52
-    lr schd: 20000
     memory (GB): 9.2
-  Name: fcn_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 71.16
       mIoU(ms+flip): 73.57
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
-- Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
+- Name: fcn_r50-d8_512x512_40k_voc12aug
   In Collection: fcn
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: fcn_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 66.97
       mIoU(ms+flip): 69.04
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
-- Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
+- Name: fcn_r101-d8_512x512_40k_voc12aug
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: fcn_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 69.91
       mIoU(ms+flip): 72.38
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
-- Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
+- Name: fcn_r101-d8_480x480_40k_pascal_context
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 100.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (480,480)
-      value: 100.7
-    lr schd: 40000
-  Name: fcn_r101-d8_480x480_40k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 44.43
       mIoU(ms+flip): 45.63
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth
-- Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
+- Name: fcn_r101-d8_480x480_80k_pascal_context
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: fcn_r101-d8_480x480_80k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 44.13
       mIoU(ms+flip): 45.26
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth
-- Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
+- Name: fcn_r101-d8_480x480_40k_pascal_context_59
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 40000
-  Name: fcn_r101-d8_480x480_40k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 48.42
       mIoU(ms+flip): 50.4
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
-- Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
+- Name: fcn_r101-d8_480x480_80k_pascal_context_59
   In Collection: fcn
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: fcn_r101-d8_480x480_80k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 49.35
       mIoU(ms+flip): 51.38
-    Task: Semantic Segmentation
+  Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
diff --git a/configs/fp16/README.md b/configs/fp16/README.md
index 881598b..bbc73cc 100644
--- a/configs/fp16/README.md
+++ b/configs/fp16/README.md
@@ -4,6 +4,13 @@
 
 <!-- [OTHERS] -->
 
+<a href="https://github.com/baidu-research/DeepBench">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmcv/blob/v1.3.14/mmcv/runner/hooks/optimizer.py#L134">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1710.03740">Mixed Precision (FP16) Training (ArXiv'2017)</a></summary>
+
 ```latex
 @article{micikevicius2017mixed,
   title={Mixed precision training},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/fp16/fp16.yml b/configs/fp16/fp16.yml
index 2be79ad..755642e 100644
--- a/configs/fp16/fp16.yml
+++ b/configs/fp16/fp16.yml
@@ -1,90 +1,99 @@
 Collections:
-- Metadata:
+- Name: fp16
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: fp16
+  Paper:
+    URL: https://arxiv.org/abs/1710.03740
+    Title: Mixed Precision Training
+  README: configs/fp16/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmcv/blob/v1.3.14/mmcv/runner/hooks/optimizer.py#L134
+    Version: v1.3.14
+  Converted From:
+    Code: https://github.com/baidu-research/DeepBench
 Models:
-- Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
+- Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
   In Collection: fp16
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 115.74
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 115.74
-    lr schd: 80000
     memory (GB): 5.37
-  Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.8
-    Task: Semantic Segmentation
+  Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230921-50245227.pth
-- Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
+- Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
   In Collection: fp16
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 114.03
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 114.03
-    lr schd: 80000
     memory (GB): 5.34
-  Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.46
-    Task: Semantic Segmentation
+  Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230919-ade37931.pth
-- Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
+- Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
   In Collection: fp16
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 259.07
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 259.07
-    lr schd: 80000
     memory (GB): 5.75
-  Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.48
-    Task: Semantic Segmentation
+  Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920-bc86dc84.pth
-- Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
+- Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
   In Collection: fp16
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 127.06
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 127.06
-    lr schd: 80000
     memory (GB): 6.35
-  Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.46
-    Task: Semantic Segmentation
+  Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920-cc58bc8d.pth
diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md
index 72f10d1..b2e5971 100644
--- a/configs/gcnet/README.md
+++ b/configs/gcnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/xvjiarui/GCNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1904.11492">GCNet (ICCVW'2019/TPAMI'2020)</a></summary>
+
 ```latex
 @inproceedings{cao2019gcnet,
   title={Gcnet: Non-local networks meet squeeze-excitation networks and beyond},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/gcnet/gcnet.yml b/configs/gcnet/gcnet.yml
index da53ac8..3bdd4ad 100644
--- a/configs/gcnet/gcnet.yml
+++ b/configs/gcnet/gcnet.yml
@@ -1,296 +1,305 @@
 Collections:
-- Metadata:
+- Name: gcnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: gcnet
+  Paper:
+    URL: https://arxiv.org/abs/1904.11492
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+  README: configs/gcnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/xvjiarui/GCNet
 Models:
-- Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: gcnet_r50-d8_512x1024_40k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 254.45
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 254.45
-    lr schd: 40000
     memory (GB): 5.8
-  Name: gcnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.69
       mIoU(ms+flip): 78.56
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: gcnet_r101-d8_512x1024_40k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 383.14
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 383.14
-    lr schd: 40000
     memory (GB): 9.2
-  Name: gcnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.28
       mIoU(ms+flip): 79.34
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
-- Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
+- Name: gcnet_r50-d8_769x769_40k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 598.8
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 598.8
-    lr schd: 40000
     memory (GB): 6.5
-  Name: gcnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.12
       mIoU(ms+flip): 80.09
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
-- Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
+- Name: gcnet_r101-d8_769x769_40k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 884.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 884.96
-    lr schd: 40000
     memory (GB): 10.5
-  Name: gcnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.95
       mIoU(ms+flip): 80.71
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
-- Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: gcnet_r50-d8_512x1024_80k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: gcnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.48
       mIoU(ms+flip): 80.01
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: gcnet_r101-d8_512x1024_80k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: gcnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.03
       mIoU(ms+flip): 79.84
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
-- Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
+- Name: gcnet_r50-d8_769x769_80k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: gcnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.68
       mIoU(ms+flip): 80.66
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
-- Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
+- Name: gcnet_r101-d8_769x769_80k_cityscapes
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: gcnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.18
       mIoU(ms+flip): 80.71
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
-- Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+- Name: gcnet_r50-d8_512x512_80k_ade20k
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.77
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.77
-    lr schd: 80000
     memory (GB): 8.5
-  Name: gcnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.47
       mIoU(ms+flip): 42.85
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
+- Name: gcnet_r101-d8_512x512_80k_ade20k
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 65.79
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 65.79
-    lr schd: 80000
     memory (GB): 12.0
-  Name: gcnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.82
       mIoU(ms+flip): 44.54
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
-- Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
+- Name: gcnet_r50-d8_512x512_160k_ade20k
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: gcnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.37
       mIoU(ms+flip): 43.52
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
+- Name: gcnet_r101-d8_512x512_160k_ade20k
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: gcnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.69
       mIoU(ms+flip): 45.21
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
-- Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
+- Name: gcnet_r50-d8_512x512_20k_voc12aug
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.83
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.83
-    lr schd: 20000
     memory (GB): 5.8
-  Name: gcnet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.42
       mIoU(ms+flip): 77.51
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
+- Name: gcnet_r101-d8_512x512_20k_voc12aug
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.57
-    lr schd: 20000
     memory (GB): 9.2
-  Name: gcnet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.41
       mIoU(ms+flip): 78.56
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
-- Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
+- Name: gcnet_r50-d8_512x512_40k_voc12aug
   In Collection: gcnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: gcnet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.24
       mIoU(ms+flip): 77.63
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
-- Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
+- Name: gcnet_r101-d8_512x512_40k_voc12aug
   In Collection: gcnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: gcnet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.84
       mIoU(ms+flip): 78.59
-    Task: Semantic Segmentation
+  Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
index e97fb38..61fb56e 100644
--- a/configs/hrnet/README.md
+++ b/configs/hrnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/HRNet/HRNet-Semantic-Segmentation">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1908.07919">HRNet (CVPR'2019)</a></summary>
+
 ```latext
 @inproceedings{SunXLW19,
   title={Deep High-Resolution Representation Learning for Human Pose Estimation},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml
index 0a9c0e2..c4a13f7 100644
--- a/configs/hrnet/hrnet.yml
+++ b/configs/hrnet/hrnet.yml
@@ -1,440 +1,449 @@
 Collections:
-- Metadata:
+- Name: hrnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
     - Pascal Context
     - Pascal Context 59
-  Name: hrnet
+  Paper:
+    URL: https://arxiv.org/abs/1908.07919
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+  README: configs/hrnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/HRNet/HRNet-Semantic-Segmentation
 Models:
-- Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
+- Name: fcn_hr18s_512x1024_40k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.12
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 42.12
-    lr schd: 40000
     memory (GB): 1.7
-  Name: fcn_hr18s_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.86
       mIoU(ms+flip): 75.91
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
-- Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
+- Name: fcn_hr18_512x1024_40k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 77.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 77.1
-    lr schd: 40000
     memory (GB): 2.9
-  Name: fcn_hr18_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.19
       mIoU(ms+flip): 78.92
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
-- Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
+- Name: fcn_hr48_512x1024_40k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 155.76
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 155.76
-    lr schd: 40000
     memory (GB): 6.2
-  Name: fcn_hr48_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.48
       mIoU(ms+flip): 79.69
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
-- Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
+- Name: fcn_hr18s_512x1024_80k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
     lr schd: 80000
-  Name: fcn_hr18s_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.31
       mIoU(ms+flip): 77.48
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
-- Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
+- Name: fcn_hr18_512x1024_80k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
     lr schd: 80000
-  Name: fcn_hr18_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.65
       mIoU(ms+flip): 80.35
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
-- Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
+- Name: fcn_hr48_512x1024_80k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
     lr schd: 80000
-  Name: fcn_hr48_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.93
       mIoU(ms+flip): 80.72
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
-- Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
+- Name: fcn_hr18s_512x1024_160k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
     lr schd: 160000
-  Name: fcn_hr18s_512x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.31
       mIoU(ms+flip): 78.31
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
-- Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
+- Name: fcn_hr18_512x1024_160k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
     lr schd: 160000
-  Name: fcn_hr18_512x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.8
       mIoU(ms+flip): 80.74
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
-- Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
+- Name: fcn_hr48_512x1024_160k_cityscapes
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
     lr schd: 160000
-  Name: fcn_hr48_512x1024_160k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.65
       mIoU(ms+flip): 81.92
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
-- Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
+- Name: fcn_hr18s_512x512_80k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 25.87
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 25.87
-    lr schd: 80000
     memory (GB): 3.8
-  Name: fcn_hr18s_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 31.38
       mIoU(ms+flip): 32.45
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
-- Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
+- Name: fcn_hr18_512x512_80k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 44.31
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 44.31
-    lr schd: 80000
     memory (GB): 4.9
-  Name: fcn_hr18_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 36.27
       mIoU(ms+flip): 37.28
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
-- Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
+- Name: fcn_hr48_512x512_80k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.1
-    lr schd: 80000
     memory (GB): 8.2
-  Name: fcn_hr48_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.9
       mIoU(ms+flip): 43.27
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
-- Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
+- Name: fcn_hr18s_512x512_160k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
     lr schd: 160000
-  Name: fcn_hr18s_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 33.07
       mIoU(ms+flip): 34.56
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
-- Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
+- Name: fcn_hr18_512x512_160k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
     lr schd: 160000
-  Name: fcn_hr18_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 36.79
       mIoU(ms+flip): 38.58
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
-- Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
+- Name: fcn_hr48_512x512_160k_ade20k
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
     lr schd: 160000
-  Name: fcn_hr48_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.02
       mIoU(ms+flip): 43.86
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
-- Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
+- Name: fcn_hr18s_512x512_20k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 23.06
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 23.06
-    lr schd: 20000
     memory (GB): 1.8
-  Name: fcn_hr18s_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 65.5
       mIoU(ms+flip): 68.89
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
-- Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
+- Name: fcn_hr18_512x512_20k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.59
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.59
-    lr schd: 20000
     memory (GB): 2.9
-  Name: fcn_hr18_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 72.3
       mIoU(ms+flip): 74.71
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
-- Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
+- Name: fcn_hr48_512x512_20k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 45.35
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 45.35
-    lr schd: 20000
     memory (GB): 6.2
-  Name: fcn_hr48_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 75.87
       mIoU(ms+flip): 78.58
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
-- Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
+- Name: fcn_hr18s_512x512_40k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
     lr schd: 40000
-  Name: fcn_hr18s_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 66.61
       mIoU(ms+flip): 70.0
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
-- Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
+- Name: fcn_hr18_512x512_40k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
     lr schd: 40000
-  Name: fcn_hr18_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 72.9
       mIoU(ms+flip): 75.59
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
-- Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
+- Name: fcn_hr48_512x512_40k_voc12aug
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
     lr schd: 40000
-  Name: fcn_hr48_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.24
       mIoU(ms+flip): 78.49
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
-- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
+- Name: fcn_hr48_480x480_40k_pascal_context
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 112.87
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (480,480)
-      value: 112.87
-    lr schd: 40000
     memory (GB): 6.1
-  Name: fcn_hr48_480x480_40k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 45.14
       mIoU(ms+flip): 47.42
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
-- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
+- Name: fcn_hr48_480x480_80k_pascal_context
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
     lr schd: 80000
-  Name: fcn_hr48_480x480_80k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 45.84
       mIoU(ms+flip): 47.84
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
-- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
+- Name: fcn_hr48_480x480_40k_pascal_context_59
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
     lr schd: 40000
-  Name: fcn_hr48_480x480_40k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 50.33
       mIoU(ms+flip): 52.83
-    Task: Semantic Segmentation
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
   Weights: 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
-- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
+- Name: fcn_hr48_480x480_80k_pascal_context_59
   In Collection: hrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
     lr schd: 80000
-  Name: fcn_hr48_480x480_80k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 51.12
       mIoU(ms+flip): 53.56
-    Task: Semantic Segmentation
+  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
diff --git a/configs/isanet/README.md b/configs/isanet/README.md
index a15bf9a..6a01fc7 100644
--- a/configs/isanet/README.md
+++ b/configs/isanet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/openseg-group/openseg.pytorch">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1907.12273">ISANet (ArXiv'2019/IJCV'2021)</a></summary>
+
 ```
 @article{huang2019isa,
   title={Interlaced Sparse Self-Attention for Semantic Segmentation},
@@ -23,6 +30,8 @@ The technical report above is also presented at:
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/isanet/isanet.yml b/configs/isanet/isanet.yml
index c73992f..113e4f1 100644
--- a/configs/isanet/isanet.yml
+++ b/configs/isanet/isanet.yml
@@ -1,360 +1,369 @@
 Collections:
-- Metadata:
+- Name: isanet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: isanet
+  Paper:
+    URL: https://arxiv.org/abs/1907.12273
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+  README: configs/isanet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+    Version: v0.18.0
+  Converted From:
+    Code: https://github.com/openseg-group/openseg.pytorch
 Models:
-- Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py
+- Name: isanet_r50-d8_512x1024_40k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 343.64
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 343.64
-    lr schd: 40000
     memory (GB): 5.869
-  Name: isanet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.49
       mIoU(ms+flip): 79.44
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
-- Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py
+- Name: isanet_r50-d8_512x1024_80k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 343.64
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 343.64
-    lr schd: 80000
     memory (GB): 5.869
-  Name: isanet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.68
       mIoU(ms+flip): 80.25
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
-- Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py
+- Name: isanet_r50-d8_769x769_40k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 649.35
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 649.35
-    lr schd: 40000
     memory (GB): 6.759
-  Name: isanet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.7
       mIoU(ms+flip): 80.28
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
-- Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py
+- Name: isanet_r50-d8_769x769_80k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 649.35
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 649.35
-    lr schd: 80000
     memory (GB): 6.759
-  Name: isanet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.29
       mIoU(ms+flip): 80.53
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
-- Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py
+- Name: isanet_r101-d8_512x1024_40k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 425.53
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 425.53
-    lr schd: 40000
     memory (GB): 9.425
-  Name: isanet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.58
       mIoU(ms+flip): 81.05
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
-- Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py
+- Name: isanet_r101-d8_512x1024_80k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 425.53
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 425.53
-    lr schd: 80000
     memory (GB): 9.425
-  Name: isanet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.32
       mIoU(ms+flip): 81.58
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
-- Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py
+- Name: isanet_r101-d8_769x769_40k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 1086.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 1086.96
-    lr schd: 40000
     memory (GB): 10.815
-  Name: isanet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.68
       mIoU(ms+flip): 80.95
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
-- Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py
+- Name: isanet_r101-d8_769x769_80k_cityscapes
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 1086.96
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 1086.96
-    lr schd: 80000
     memory (GB): 10.815
-  Name: isanet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.61
       mIoU(ms+flip): 81.59
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
-- Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py
+- Name: isanet_r50-d8_512x512_80k_ade20k
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 44.35
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 44.35
-    lr schd: 80000
     memory (GB): 9.0
-  Name: isanet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.12
       mIoU(ms+flip): 42.35
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
-- Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py
+- Name: isanet_r50-d8_512x512_160k_ade20k
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 44.35
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 44.35
-    lr schd: 160000
     memory (GB): 9.0
-  Name: isanet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.59
       mIoU(ms+flip): 43.07
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
-- Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py
+- Name: isanet_r101-d8_512x512_80k_ade20k
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 94.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 94.7
-    lr schd: 80000
     memory (GB): 12.562
-  Name: isanet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.51
       mIoU(ms+flip): 44.38
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
-- Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py
+- Name: isanet_r101-d8_512x512_160k_ade20k
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 94.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 94.7
-    lr schd: 160000
     memory (GB): 12.562
-  Name: isanet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.8
       mIoU(ms+flip): 45.4
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
-- Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py
+- Name: isanet_r50-d8_512x512_20k_voc12aug
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 43.33
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 43.33
-    lr schd: 20000
     memory (GB): 5.9
-  Name: isanet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.78
       mIoU(ms+flip): 77.79
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
-- Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py
+- Name: isanet_r50-d8_512x512_40k_voc12aug
   In Collection: isanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 43.33
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 43.33
-    lr schd: 40000
     memory (GB): 5.9
-  Name: isanet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.2
       mIoU(ms+flip): 77.22
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
-- Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py
+- Name: isanet_r101-d8_512x512_20k_voc12aug
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 134.77
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 134.77
-    lr schd: 20000
     memory (GB): 9.465
-  Name: isanet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.46
       mIoU(ms+flip): 79.16
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
-- Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py
+- Name: isanet_r101-d8_512x512_40k_voc12aug
   In Collection: isanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 134.77
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 134.77
-    lr schd: 40000
     memory (GB): 9.465
-  Name: isanet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.12
       mIoU(ms+flip): 79.04
-    Task: Semantic Segmentation
+  Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth
diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md
index 7356a0e..7bdd2bd 100644
--- a/configs/mobilenet_v2/README.md
+++ b/configs/mobilenet_v2/README.md
@@ -4,6 +4,13 @@
 
 <!-- [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_v2.py#L14">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1801.04381">MobileNetV2 (CVPR'2018)</a></summary>
+
 ```latex
 @inproceedings{sandler2018mobilenetv2,
   title={Mobilenetv2: Inverted residuals and linear bottlenecks},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/mobilenet_v2/mobilenet_v2.yml b/configs/mobilenet_v2/mobilenet_v2.yml
index b23a02a..b8aa46f 100644
--- a/configs/mobilenet_v2/mobilenet_v2.yml
+++ b/configs/mobilenet_v2/mobilenet_v2.yml
@@ -1,175 +1,184 @@
 Collections:
-- Metadata:
+- Name: mobilenet_v2
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20k
-  Name: mobilenet_v2
+  Paper:
+    URL: https://arxiv.org/abs/1801.04381
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+  README: configs/mobilenet_v2/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
-- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
+- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 70.42
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 70.42
-    lr schd: 80000
     memory (GB): 3.4
-  Name: fcn_m-v2-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 61.54
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
-- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 89.29
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 89.29
-    lr schd: 80000
     memory (GB): 3.6
-  Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 70.23
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
-- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 119.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 119.05
-    lr schd: 80000
     memory (GB): 3.9
-  Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 73.84
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
-- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 119.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 119.05
-    lr schd: 80000
     memory (GB): 5.1
-  Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.2
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
-- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
+- Name: fcn_m-v2-d8_512x512_160k_ade20k
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 15.53
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 15.53
-    lr schd: 160000
     memory (GB): 6.5
-  Name: fcn_m-v2-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 19.71
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
-- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
+- Name: pspnet_m-v2-d8_512x512_160k_ade20k
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 17.33
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 17.33
-    lr schd: 160000
     memory (GB): 6.5
-  Name: pspnet_m-v2-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 29.68
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
-- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
+- Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 25.06
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 25.06
-    lr schd: 160000
     memory (GB): 6.8
-  Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 34.08
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
-- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
+- Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
   In Collection: mobilenet_v2
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 23.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 23.2
-    lr schd: 160000
     memory (GB): 8.2
-  Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 34.02
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md
index a843d35..89a0344 100644
--- a/configs/mobilenet_v3/README.md
+++ b/configs/mobilenet_v3/README.md
@@ -4,6 +4,13 @@
 
 <!-- [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>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1801.04381">MobileNetV3 (ICCV'2019)</a></summary>
+
 ```latex
 @inproceedings{Howard_2019_ICCV,
   title={Searching for MobileNetV3},
@@ -16,6 +23,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/mobilenet_v3/mobilenet_v3.yml b/configs/mobilenet_v3/mobilenet_v3.yml
index 68e6368..b047408 100644
--- a/configs/mobilenet_v3/mobilenet_v3.yml
+++ b/configs/mobilenet_v3/mobilenet_v3.yml
@@ -1,94 +1,103 @@
 Collections:
-- Metadata:
+- Name: mobilenet_v3
+  Metadata:
     Training Data:
     - Cityscapes
-  Name: mobilenet_v3
+  Paper:
+    URL: https://arxiv.org/abs/1801.04381
+    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:
-- Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
+- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
   In Collection: mobilenet_v3
   Metadata:
     backbone: M-V3-D8
     crop size: (512,1024)
+    lr schd: 320000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 65.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 65.7
-    lr schd: 320000
     memory (GB): 8.9
-  Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 69.54
       mIoU(ms+flip): 70.89
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
-- Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
+- Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
   In Collection: mobilenet_v3
   Metadata:
     backbone: M-V3-D8 (scratch)
     crop size: (512,1024)
+    lr schd: 320000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 67.7
-    lr schd: 320000
     memory (GB): 8.9
-  Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 67.87
       mIoU(ms+flip): 69.78
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
-- Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
+- Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
   In Collection: mobilenet_v3
   Metadata:
     backbone: M-V3s-D8
     crop size: (512,1024)
+    lr schd: 320000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.3
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 42.3
-    lr schd: 320000
     memory (GB): 5.3
-  Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 64.11
       mIoU(ms+flip): 66.42
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
-- Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
+- Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
   In Collection: mobilenet_v3
   Metadata:
     backbone: M-V3s-D8 (scratch)
     crop size: (512,1024)
+    lr schd: 320000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 40.82
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 40.82
-    lr schd: 320000
     memory (GB): 5.3
-  Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 62.74
       mIoU(ms+flip): 65.01
-    Task: Semantic Segmentation
+  Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
diff --git a/configs/nonlocal_net/README.md b/configs/nonlocal_net/README.md
index b98d6d5..643bc70 100644
--- a/configs/nonlocal_net/README.md
+++ b/configs/nonlocal_net/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/facebookresearch/video-nonlocal-net">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1711.07971">NonLocal Net (CVPR'2018)</a></summary>
+
 ```latex
 @inproceedings{wang2018non,
   title={Non-local neural networks},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/nonlocal_net/nonlocal_net.yml b/configs/nonlocal_net/nonlocal_net.yml
index daf56bb..7402294 100644
--- a/configs/nonlocal_net/nonlocal_net.yml
+++ b/configs/nonlocal_net/nonlocal_net.yml
@@ -1,292 +1,301 @@
 Collections:
-- Metadata:
+- Name: nonlocal_net
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: nonlocal_net
+  Paper:
+    URL: https://arxiv.org/abs/1711.07971
+    Title: Non-local Neural Networks
+  README: configs/nonlocal_net/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/facebookresearch/video-nonlocal-net
 Models:
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py
+- Name: nonlocal_r50-d8_512x1024_40k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 367.65
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 367.65
-    lr schd: 40000
     memory (GB): 7.4
-  Name: nonlocal_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.24
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
+- Name: nonlocal_r101-d8_512x1024_40k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 512.82
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 512.82
-    lr schd: 40000
     memory (GB): 10.9
-  Name: nonlocal_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.66
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
+- Name: nonlocal_r50-d8_769x769_40k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 657.89
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 657.89
-    lr schd: 40000
     memory (GB): 8.9
-  Name: nonlocal_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.33
       mIoU(ms+flip): 79.92
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py
+- Name: nonlocal_r101-d8_769x769_40k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 952.38
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 952.38
-    lr schd: 40000
     memory (GB): 12.8
-  Name: nonlocal_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.57
       mIoU(ms+flip): 80.29
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
+- Name: nonlocal_r50-d8_512x1024_80k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: nonlocal_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.01
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py
+- Name: nonlocal_r101-d8_512x1024_80k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: nonlocal_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.93
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py
+- Name: nonlocal_r50-d8_769x769_80k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: nonlocal_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.05
       mIoU(ms+flip): 80.68
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py
+- Name: nonlocal_r101-d8_769x769_80k_cityscapes
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: nonlocal_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.4
       mIoU(ms+flip): 80.85
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py
+- Name: nonlocal_r50-d8_512x512_80k_ade20k
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 46.79
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 46.79
-    lr schd: 80000
     memory (GB): 9.1
-  Name: nonlocal_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 40.75
       mIoU(ms+flip): 42.05
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py
+- Name: nonlocal_r101-d8_512x512_80k_ade20k
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 71.58
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 71.58
-    lr schd: 80000
     memory (GB): 12.6
-  Name: nonlocal_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.9
       mIoU(ms+flip): 44.27
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
+- Name: nonlocal_r50-d8_512x512_160k_ade20k
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: nonlocal_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.03
       mIoU(ms+flip): 43.04
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
+- Name: nonlocal_r101-d8_512x512_160k_ade20k
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: nonlocal_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.63
       mIoU(ms+flip): 45.79
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502-7881aa1a.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
+- Name: nonlocal_r50-d8_512x512_20k_voc12aug
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.15
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.15
-    lr schd: 20000
     memory (GB): 6.4
-  Name: nonlocal_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.2
       mIoU(ms+flip): 77.12
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py
+- Name: nonlocal_r101-d8_512x512_20k_voc12aug
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 71.38
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 71.38
-    lr schd: 20000
     memory (GB): 9.8
-  Name: nonlocal_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.15
       mIoU(ms+flip): 78.86
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
-- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py
+- Name: nonlocal_r50-d8_512x512_40k_voc12aug
   In Collection: nonlocal_net
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: nonlocal_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.65
       mIoU(ms+flip): 77.47
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
-- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py
+- Name: nonlocal_r101-d8_512x512_40k_voc12aug
   In Collection: nonlocal_net
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: nonlocal_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.27
       mIoU(ms+flip): 79.12
-    Task: Semantic Segmentation
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
diff --git a/configs/ocrnet/README.md b/configs/ocrnet/README.md
index 68b4bb3..bde8964 100644
--- a/configs/ocrnet/README.md
+++ b/configs/ocrnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/openseg-group/OCNet.pytorch">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1909.11065">OCRNet (ECCV'2020)</a></summary>
+
 ```latex
 @article{YuanW18,
   title={Ocnet: Object context network for scene parsing},
@@ -20,6 +27,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/ocrnet/ocrnet.yml b/configs/ocrnet/ocrnet.yml
index 09db262..635580c 100644
--- a/configs/ocrnet/ocrnet.yml
+++ b/configs/ocrnet/ocrnet.yml
@@ -1,431 +1,438 @@
 Collections:
-- Metadata:
+- Name: ocrnet
+  Metadata:
     Training Data:
     - Cityscapes
-    - ' HRNet backbone'
-    - ' ResNet backbone'
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: ocrnet
+  Paper:
+    URL: https://arxiv.org/abs/1909.11065
+    Title: Object-Contextual Representations for Semantic Segmentation
+  README: configs/ocrnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/openseg-group/OCNet.pytorch
 Models:
-- Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
+- Name: ocrnet_hr18s_512x1024_40k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 95.69
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 95.69
-    lr schd: 40000
     memory (GB): 3.5
-  Name: ocrnet_hr18s_512x1024_40k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 74.3
       mIoU(ms+flip): 75.95
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
+- Name: ocrnet_hr18_512x1024_40k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 133.33
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 133.33
-    lr schd: 40000
     memory (GB): 4.7
-  Name: ocrnet_hr18_512x1024_40k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 77.72
       mIoU(ms+flip): 79.49
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
+- Name: ocrnet_hr48_512x1024_40k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 236.97
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 236.97
-    lr schd: 40000
     memory (GB): 8.0
-  Name: ocrnet_hr48_512x1024_40k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 80.58
       mIoU(ms+flip): 81.79
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
+- Name: ocrnet_hr18s_512x1024_80k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
     lr schd: 80000
-  Name: ocrnet_hr18s_512x1024_80k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 77.16
       mIoU(ms+flip): 78.66
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
+- Name: ocrnet_hr18_512x1024_80k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
     lr schd: 80000
-  Name: ocrnet_hr18_512x1024_80k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 78.57
       mIoU(ms+flip): 80.46
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
+- Name: ocrnet_hr48_512x1024_80k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
     lr schd: 80000
-  Name: ocrnet_hr48_512x1024_80k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 80.7
       mIoU(ms+flip): 81.87
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
+- Name: ocrnet_hr18s_512x1024_160k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
     lr schd: 160000
-  Name: ocrnet_hr18s_512x1024_160k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 78.45
       mIoU(ms+flip): 79.97
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
+- Name: ocrnet_hr18_512x1024_160k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
     lr schd: 160000
-  Name: ocrnet_hr18_512x1024_160k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 79.47
       mIoU(ms+flip): 80.91
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
+- Name: ocrnet_hr48_512x1024_160k_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
     lr schd: 160000
-  Name: ocrnet_hr48_512x1024_160k_cityscapes
   Results:
-    Dataset: ' HRNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 81.35
       mIoU(ms+flip): 82.7
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
-- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
+- Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 40000
-  Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
   Results:
-    Dataset: ' ResNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 80.09
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth
-- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
+- Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 331.13
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 331.13
-    lr schd: 40000
     memory (GB): 8.8
-  Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
   Results:
-    Dataset: ' ResNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 80.3
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth
-- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
+- Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
   In Collection: ocrnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 331.13
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 331.13
-    lr schd: 80000
     memory (GB): 8.8
-  Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
   Results:
-    Dataset: ' ResNet backbone'
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
     Metrics:
       mIoU: 80.81
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
+- Name: ocrnet_hr18s_512x512_80k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 34.51
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 34.51
-    lr schd: 80000
     memory (GB): 6.7
-  Name: ocrnet_hr18s_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 35.06
       mIoU(ms+flip): 35.8
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
+- Name: ocrnet_hr18_512x512_80k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 52.83
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 52.83
-    lr schd: 80000
     memory (GB): 7.9
-  Name: ocrnet_hr18_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 37.79
       mIoU(ms+flip): 39.16
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
+- Name: ocrnet_hr48_512x512_80k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 58.86
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 58.86
-    lr schd: 80000
     memory (GB): 11.2
-  Name: ocrnet_hr48_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.0
       mIoU(ms+flip): 44.3
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
+- Name: ocrnet_hr18s_512x512_160k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
     lr schd: 160000
-  Name: ocrnet_hr18s_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 37.19
       mIoU(ms+flip): 38.4
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+- Name: ocrnet_hr18_512x512_160k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
     lr schd: 160000
-  Name: ocrnet_hr18_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 39.32
       mIoU(ms+flip): 40.8
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
+- Name: ocrnet_hr48_512x512_160k_ade20k
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
     lr schd: 160000
-  Name: ocrnet_hr48_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.25
       mIoU(ms+flip): 44.88
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
+- Name: ocrnet_hr18s_512x512_20k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 31.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 31.7
-    lr schd: 20000
     memory (GB): 3.5
-  Name: ocrnet_hr18s_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 71.7
       mIoU(ms+flip): 73.84
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
+- Name: ocrnet_hr18_512x512_20k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 50.23
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 50.23
-    lr schd: 20000
     memory (GB): 4.7
-  Name: ocrnet_hr18_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 74.75
       mIoU(ms+flip): 77.11
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
+- Name: ocrnet_hr48_512x512_20k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 56.09
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 56.09
-    lr schd: 20000
     memory (GB): 8.1
-  Name: ocrnet_hr48_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.72
       mIoU(ms+flip): 79.87
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
-- Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
+- Name: ocrnet_hr18s_512x512_40k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
     lr schd: 40000
-  Name: ocrnet_hr18s_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 72.76
       mIoU(ms+flip): 74.6
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
-- Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
+- Name: ocrnet_hr18_512x512_40k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
     lr schd: 40000
-  Name: ocrnet_hr18_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 74.98
       mIoU(ms+flip): 77.4
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
-- Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
+- Name: ocrnet_hr48_512x512_40k_voc12aug
   In Collection: ocrnet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
     lr schd: 40000
-  Name: ocrnet_hr48_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.14
       mIoU(ms+flip): 79.71
-    Task: Semantic Segmentation
+  Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md
index 9031f2b..4146eb4 100644
--- a/configs/point_rend/README.md
+++ b/configs/point_rend/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1912.08193">PointRend (CVPR'2020)</a></summary>
+
 ```
 @inproceedings{kirillov2020pointrend,
   title={Pointrend: Image segmentation as rendering},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/point_rend/point_rend.yml b/configs/point_rend/point_rend.yml
index 064af53..835bbdb 100644
--- a/configs/point_rend/point_rend.yml
+++ b/configs/point_rend/point_rend.yml
@@ -1,95 +1,104 @@
 Collections:
-- Metadata:
+- Name: point_rend
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: point_rend
+  Paper:
+    URL: https://arxiv.org/abs/1912.08193
+    Title: 'PointRend: Image Segmentation as Rendering'
+  README: configs/point_rend/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend
 Models:
-- Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
+- Name: pointrend_r50_512x1024_80k_cityscapes
   In Collection: point_rend
   Metadata:
     backbone: R-50
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 117.92
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 117.92
-    lr schd: 80000
     memory (GB): 3.1
-  Name: pointrend_r50_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 76.47
       mIoU(ms+flip): 78.13
-    Task: Semantic Segmentation
+  Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
-- Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
+- Name: pointrend_r101_512x1024_80k_cityscapes
   In Collection: point_rend
   Metadata:
     backbone: R-101
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 142.86
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 142.86
-    lr schd: 80000
     memory (GB): 4.2
-  Name: pointrend_r101_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.3
       mIoU(ms+flip): 79.97
-    Task: Semantic Segmentation
+  Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
-- Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
+- Name: pointrend_r50_512x512_160k_ade20k
   In Collection: point_rend
   Metadata:
     backbone: R-50
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 57.77
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 57.77
-    lr schd: 160000
     memory (GB): 5.1
-  Name: pointrend_r50_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 37.64
       mIoU(ms+flip): 39.17
-    Task: Semantic Segmentation
+  Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
-- Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
+- Name: pointrend_r101_512x512_160k_ade20k
   In Collection: point_rend
   Metadata:
     backbone: R-101
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 64.52
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 64.52
-    lr schd: 160000
     memory (GB): 6.1
-  Name: pointrend_r101_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 40.02
       mIoU(ms+flip): 41.6
-    Task: Semantic Segmentation
+  Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
diff --git a/configs/psanet/README.md b/configs/psanet/README.md
index 01ed322..bd9f173 100644
--- a/configs/psanet/README.md
+++ b/configs/psanet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/hszhao/PSANet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf">PSANet (ECCV'2018)</a></summary>
+
 ```latex
 @inproceedings{zhao2018psanet,
   title={Psanet: Point-wise spatial attention network for scene parsing},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/psanet/psanet.yml b/configs/psanet/psanet.yml
index ae06948..a263b3f 100644
--- a/configs/psanet/psanet.yml
+++ b/configs/psanet/psanet.yml
@@ -1,296 +1,305 @@
 Collections:
-- Metadata:
+- Name: psanet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: psanet
+  Paper:
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+  README: configs/psanet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/hszhao/PSANet
 Models:
-- Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
+- Name: psanet_r50-d8_512x1024_40k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 315.46
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 315.46
-    lr schd: 40000
     memory (GB): 7.0
-  Name: psanet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.63
       mIoU(ms+flip): 79.04
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
-- Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
+- Name: psanet_r101-d8_512x1024_40k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 454.55
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 454.55
-    lr schd: 40000
     memory (GB): 10.5
-  Name: psanet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.14
       mIoU(ms+flip): 80.19
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
-- Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
+- Name: psanet_r50-d8_769x769_40k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 714.29
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 714.29
-    lr schd: 40000
     memory (GB): 7.9
-  Name: psanet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.99
       mIoU(ms+flip): 79.64
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
-- Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
+- Name: psanet_r101-d8_769x769_40k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 1020.41
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 1020.41
-    lr schd: 40000
     memory (GB): 11.9
-  Name: psanet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.43
       mIoU(ms+flip): 80.26
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
-- Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
+- Name: psanet_r50-d8_512x1024_80k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: psanet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.24
       mIoU(ms+flip): 78.69
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
-- Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
+- Name: psanet_r101-d8_512x1024_80k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: psanet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.31
       mIoU(ms+flip): 80.53
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
-- Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
+- Name: psanet_r50-d8_769x769_80k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: psanet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.31
       mIoU(ms+flip): 80.91
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
-- Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
+- Name: psanet_r101-d8_769x769_80k_cityscapes
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: psanet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.69
       mIoU(ms+flip): 80.89
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
-- Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
+- Name: psanet_r50-d8_512x512_80k_ade20k
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 52.88
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 52.88
-    lr schd: 80000
     memory (GB): 9.0
-  Name: psanet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.14
       mIoU(ms+flip): 41.91
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
-- Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
+- Name: psanet_r101-d8_512x512_80k_ade20k
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 76.16
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 76.16
-    lr schd: 80000
     memory (GB): 12.5
-  Name: psanet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.8
       mIoU(ms+flip): 44.75
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
-- Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
+- Name: psanet_r50-d8_512x512_160k_ade20k
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: psanet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.67
       mIoU(ms+flip): 42.95
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
-- Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
+- Name: psanet_r101-d8_512x512_160k_ade20k
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: psanet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.74
       mIoU(ms+flip): 45.38
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
-- Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
+- Name: psanet_r50-d8_512x512_20k_voc12aug
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 54.82
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 54.82
-    lr schd: 20000
     memory (GB): 6.9
-  Name: psanet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.39
       mIoU(ms+flip): 77.34
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
-- Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
+- Name: psanet_r101-d8_512x512_20k_voc12aug
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 79.18
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 79.18
-    lr schd: 20000
     memory (GB): 10.4
-  Name: psanet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.91
       mIoU(ms+flip): 79.3
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
-- Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
+- Name: psanet_r50-d8_512x512_40k_voc12aug
   In Collection: psanet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: psanet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.3
       mIoU(ms+flip): 77.35
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
-- Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
+- Name: psanet_r101-d8_512x512_40k_voc12aug
   In Collection: psanet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: psanet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.73
       mIoU(ms+flip): 79.05
-    Task: Semantic Segmentation
+  Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md
index 5bf8da3..72b280a 100644
--- a/configs/pspnet/README.md
+++ b/configs/pspnet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/hszhao/PSPNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1612.01105">PSPNet (CVPR'2017)</a></summary>
+
 ```latex
 @inproceedings{zhao2017pspnet,
   title={Pyramid Scene Parsing Network},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml
index 050751e..4b3cd1f 100644
--- a/configs/pspnet/pspnet.yml
+++ b/configs/pspnet/pspnet.yml
@@ -1,5 +1,6 @@
 Collections:
-- Metadata:
+- Name: pspnet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
@@ -9,705 +10,713 @@ Collections:
     - Dark Zurich and Nighttime Driving
     - COCO-Stuff 10k
     - COCO-Stuff 164k
-  Name: pspnet
+  Paper:
+    URL: https://arxiv.org/abs/1612.01105
+    Title: Pyramid Scene Parsing Network
+  README: configs/pspnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/hszhao/PSPNet
 Models:
-- Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
+- Name: pspnet_r50-d8_512x1024_40k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 245.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 245.7
-    lr schd: 40000
     memory (GB): 6.1
-  Name: pspnet_r50-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.85
       mIoU(ms+flip): 79.18
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
+- Name: pspnet_r101-d8_512x1024_40k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 373.13
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 373.13
-    lr schd: 40000
     memory (GB): 9.6
-  Name: pspnet_r101-d8_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.34
       mIoU(ms+flip): 79.74
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
-- Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
+- Name: pspnet_r50-d8_769x769_40k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 568.18
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 568.18
-    lr schd: 40000
     memory (GB): 6.9
-  Name: pspnet_r50-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.26
       mIoU(ms+flip): 79.88
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
-- Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
+- Name: pspnet_r101-d8_769x769_40k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 869.57
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 869.57
-    lr schd: 40000
     memory (GB): 10.9
-  Name: pspnet_r101-d8_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.08
       mIoU(ms+flip): 80.28
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
-- Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r18-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 63.65
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 63.65
-    lr schd: 80000
     memory (GB): 1.7
-  Name: pspnet_r18-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 74.87
       mIoU(ms+flip): 76.04
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r50-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: pspnet_r50-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.55
       mIoU(ms+flip): 79.79
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r101-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
     lr schd: 80000
-  Name: pspnet_r101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.76
       mIoU(ms+flip): 81.01
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
-- Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r18-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 161.29
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 161.29
-    lr schd: 80000
     memory (GB): 1.9
-  Name: pspnet_r18-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.9
       mIoU(ms+flip): 77.86
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
-- Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r50-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: pspnet_r50-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.59
       mIoU(ms+flip): 80.69
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
-- Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r101-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
     lr schd: 80000
-  Name: pspnet_r101-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.77
       mIoU(ms+flip): 81.06
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
-- Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r18b-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 61.43
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 61.43
-    lr schd: 80000
     memory (GB): 1.5
-  Name: pspnet_r18b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 74.23
       mIoU(ms+flip): 75.79
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
-- Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r50b-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 232.56
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 232.56
-    lr schd: 80000
     memory (GB): 6.0
-  Name: pspnet_r50b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.22
       mIoU(ms+flip): 79.46
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
-- Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_r101b-d8_512x1024_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 362.32
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 362.32
-    lr schd: 80000
     memory (GB): 9.5
-  Name: pspnet_r101b-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.69
       mIoU(ms+flip): 80.79
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
-- Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r18b-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 156.01
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 156.01
-    lr schd: 80000
     memory (GB): 1.7
-  Name: pspnet_r18b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 74.92
       mIoU(ms+flip): 76.9
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
-- Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r50b-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 531.91
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 531.91
-    lr schd: 80000
     memory (GB): 6.8
-  Name: pspnet_r50b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.5
       mIoU(ms+flip): 79.96
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
-- Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
+- Name: pspnet_r101b-d8_769x769_80k_cityscapes
   In Collection: pspnet
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 854.7
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 854.7
-    lr schd: 80000
     memory (GB): 10.8
-  Name: pspnet_r101b-d8_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.87
       mIoU(ms+flip): 80.04
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
+- Name: pspnet_r50-d8_512x512_80k_ade20k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.5
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.5
-    lr schd: 80000
     memory (GB): 8.5
-  Name: pspnet_r50-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 41.13
       mIoU(ms+flip): 41.94
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
+- Name: pspnet_r101-d8_512x512_80k_ade20k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 65.36
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 65.36
-    lr schd: 80000
     memory (GB): 12.0
-  Name: pspnet_r101-d8_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.57
       mIoU(ms+flip): 44.35
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
+- Name: pspnet_r50-d8_512x512_160k_ade20k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: pspnet_r50-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.48
       mIoU(ms+flip): 43.44
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
+- Name: pspnet_r101-d8_512x512_160k_ade20k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: pspnet_r101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.39
       mIoU(ms+flip): 45.35
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
+- Name: pspnet_r50-d8_512x512_20k_voc12aug
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.39
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.39
-    lr schd: 20000
     memory (GB): 6.1
-  Name: pspnet_r50-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 76.78
       mIoU(ms+flip): 77.61
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
+- Name: pspnet_r101-d8_512x512_20k_voc12aug
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 66.58
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 66.58
-    lr schd: 20000
     memory (GB): 9.6
-  Name: pspnet_r101-d8_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.47
       mIoU(ms+flip): 79.25
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
+- Name: pspnet_r50-d8_512x512_40k_voc12aug
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: pspnet_r50-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.29
       mIoU(ms+flip): 78.48
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
-- Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
+- Name: pspnet_r101-d8_512x512_40k_voc12aug
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: pspnet_r101-d8_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 78.52
       mIoU(ms+flip): 79.57
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
-- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
+- Name: pspnet_r101-d8_480x480_40k_pascal_context
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 103.31
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (480,480)
-      value: 103.31
-    lr schd: 40000
     memory (GB): 8.8
-  Name: pspnet_r101-d8_480x480_40k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 46.6
       mIoU(ms+flip): 47.78
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
-- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
+- Name: pspnet_r101-d8_480x480_80k_pascal_context
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: pspnet_r101-d8_480x480_80k_pascal_context
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context
     Metrics:
       mIoU: 46.03
       mIoU(ms+flip): 47.15
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
-- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
+- Name: pspnet_r101-d8_480x480_40k_pascal_context_59
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 40000
-  Name: pspnet_r101-d8_480x480_40k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 52.02
       mIoU(ms+flip): 53.54
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
-- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
+- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
     lr schd: 80000
-  Name: pspnet_r101-d8_480x480_80k_pascal_context_59
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal Context 59
     Metrics:
       mIoU: 52.47
       mIoU(ms+flip): 53.99
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
-- Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py
+- Name: pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 48.78
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 48.78
-    lr schd: 20000
     memory (GB): 9.6
-  Name: pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 35.69
       mIoU(ms+flip): 36.62
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py
+- Name: pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 90.09
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 90.09
-    lr schd: 20000
     memory (GB): 13.2
-  Name: pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 37.26
       mIoU(ms+flip): 38.52
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py
+- Name: pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 36.33
       mIoU(ms+flip): 37.24
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py
+- Name: pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 40000
-  Name: pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 10k
     Metrics:
       mIoU: 37.76
       mIoU(ms+flip): 38.86
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py
+- Name: pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 48.78
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 48.78
-    lr schd: 80000
     memory (GB): 9.6
-  Name: pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 38.8
       mIoU(ms+flip): 39.19
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py
+- Name: pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 90.09
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 90.09
-    lr schd: 80000
     memory (GB): 13.2
-  Name: pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 40.34
       mIoU(ms+flip): 40.79
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py
+- Name: pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 39.64
       mIoU(ms+flip): 39.97
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py
+- Name: pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 160000
-  Name: pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 41.28
       mIoU(ms+flip): 41.66
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py
+- Name: pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
     lr schd: 320000
-  Name: pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 40.53
       mIoU(ms+flip): 40.75
-    Task: Semantic Segmentation
+  Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py
   Weights: 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
-- Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py
+- Name: pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k
   In Collection: pspnet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
     lr schd: 320000
-  Name: pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k
   Results:
+  - Task: Semantic Segmentation
     Dataset: COCO-Stuff 164k
     Metrics:
       mIoU: 41.95
       mIoU(ms+flip): 42.42
-    Task: Semantic Segmentation
+  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
diff --git a/configs/resnest/README.md b/configs/resnest/README.md
index b610c14..a8710e6 100644
--- a/configs/resnest/README.md
+++ b/configs/resnest/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/zhanghang1989/ResNeSt">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2004.08955">ResNeSt (ArXiv'2020)</a></summary>
+
 ```latex
 @article{zhang2020resnest,
 title={ResNeSt: Split-Attention Networks},
@@ -13,6 +20,8 @@ year={2020}
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/resnest/resnest.yml b/configs/resnest/resnest.yml
index 417cce9..624929a 100644
--- a/configs/resnest/resnest.yml
+++ b/configs/resnest/resnest.yml
@@ -1,183 +1,192 @@
 Collections:
-- Metadata:
+- Name: resnest
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20k
-  Name: resnest
+  Paper:
+    URL: https://arxiv.org/abs/2004.08955
+    Title: 'ResNeSt: Split-Attention Networks'
+  README: configs/resnest/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/zhanghang1989/ResNeSt
 Models:
-- Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
+- Name: fcn_s101-d8_512x1024_80k_cityscapes
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 418.41
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 418.41
-    lr schd: 80000
     memory (GB): 11.4
-  Name: fcn_s101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.56
       mIoU(ms+flip): 78.98
-    Task: Semantic Segmentation
+  Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
-- Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
+- Name: pspnet_s101-d8_512x1024_80k_cityscapes
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 396.83
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 396.83
-    lr schd: 80000
     memory (GB): 11.8
-  Name: pspnet_s101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.57
       mIoU(ms+flip): 79.19
-    Task: Semantic Segmentation
+  Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
-- Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 531.91
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 531.91
-    lr schd: 80000
     memory (GB): 11.9
-  Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.67
       mIoU(ms+flip): 80.51
-    Task: Semantic Segmentation
+  Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
-- Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
+- Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 423.73
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 423.73
-    lr schd: 80000
     memory (GB): 13.2
-  Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.62
       mIoU(ms+flip): 80.27
-    Task: Semantic Segmentation
+  Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
-- Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
+- Name: fcn_s101-d8_512x512_160k_ade20k
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 77.76
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 77.76
-    lr schd: 160000
     memory (GB): 14.2
-  Name: fcn_s101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 45.62
       mIoU(ms+flip): 46.16
-    Task: Semantic Segmentation
+  Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
-- Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
+- Name: pspnet_s101-d8_512x512_160k_ade20k
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 76.8
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 76.8
-    lr schd: 160000
     memory (GB): 14.2
-  Name: pspnet_s101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 45.44
       mIoU(ms+flip): 46.28
-    Task: Semantic Segmentation
+  Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
-- Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
+- Name: deeplabv3_s101-d8_512x512_160k_ade20k
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 107.76
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 107.76
-    lr schd: 160000
     memory (GB): 14.6
-  Name: deeplabv3_s101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 45.71
       mIoU(ms+flip): 46.59
-    Task: Semantic Segmentation
+  Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
-- Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
+- Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
   In Collection: resnest
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 83.61
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 83.61
-    lr schd: 160000
     memory (GB): 16.2
-  Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 46.47
       mIoU(ms+flip): 47.27
-    Task: Semantic Segmentation
+  Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
diff --git a/configs/segformer/README.md b/configs/segformer/README.md
index d325589..58c6a1c 100644
--- a/configs/segformer/README.md
+++ b/configs/segformer/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/NVlabs/SegFormer">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2105.15203">SegFormer (ArXiv'2021)</a></summary>
+
 ```latex
 @article{xie2021segformer,
   title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
diff --git a/configs/segformer/segformer.yml b/configs/segformer/segformer.yml
index f945ecc..e6e514a 100644
--- a/configs/segformer/segformer.yml
+++ b/configs/segformer/segformer.yml
@@ -1,160 +1,169 @@
 Collections:
-- Metadata:
+- Name: segformer
+  Metadata:
     Training Data:
     - ADE20k
-  Name: segformer
+  Paper:
+    URL: https://arxiv.org/abs/2105.15203
+    Title: resize image to multiple of 32, improve SegFormer by 0.5-1.0 mIoU.
+  README: configs/segformer/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/NVlabs/SegFormer
 Models:
-- Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py
+- Name: segformer_mit-b0_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B0
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 19.49
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 19.49
-    lr schd: 160000
     memory (GB): 2.1
-  Name: segformer_mit-b0_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 37.41
       mIoU(ms+flip): 38.34
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth
-- Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py
+- Name: segformer_mit-b1_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B1
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 20.98
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 20.98
-    lr schd: 160000
     memory (GB): 2.6
-  Name: segformer_mit-b1_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 40.97
       mIoU(ms+flip): 42.54
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth
-- Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py
+- Name: segformer_mit-b2_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B2
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 32.38
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 32.38
-    lr schd: 160000
     memory (GB): 3.6
-  Name: segformer_mit-b2_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 45.58
       mIoU(ms+flip): 47.03
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth
-- Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py
+- Name: segformer_mit-b3_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B3
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 45.23
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 45.23
-    lr schd: 160000
     memory (GB): 4.8
-  Name: segformer_mit-b3_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 47.82
       mIoU(ms+flip): 48.81
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth
-- Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py
+- Name: segformer_mit-b4_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B4
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 64.72
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 64.72
-    lr schd: 160000
     memory (GB): 6.1
-  Name: segformer_mit-b4_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 48.46
       mIoU(ms+flip): 49.76
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth
-- Config: configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py
+- Name: segformer_mit-b5_512x512_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B5
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 84.1
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 84.1
-    lr schd: 160000
     memory (GB): 7.2
-  Name: segformer_mit-b5_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 49.13
       mIoU(ms+flip): 50.22
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth
-- Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py
+- Name: segformer_mit-b5_640x640_160k_ade20k
   In Collection: segformer
   Metadata:
     backbone: MIT-B5
     crop size: (640,640)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 88.5
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (640,640)
-      value: 88.5
-    lr schd: 160000
     memory (GB): 11.5
-  Name: segformer_mit-b5_640x640_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20k
     Metrics:
       mIoU: 49.62
       mIoU(ms+flip): 50.36
-    Task: Semantic Segmentation
+  Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth
diff --git a/configs/sem_fpn/README.md b/configs/sem_fpn/README.md
index c59698d..d9c13a1 100644
--- a/configs/sem_fpn/README.md
+++ b/configs/sem_fpn/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/facebookresearch/detectron2">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1901.02446">Semantic FPN (CVPR'2019)</a></summary>
+
 ```latex
 @article{Kirillov_2019,
    title={Panoptic Feature Pyramid Networks},
@@ -18,6 +25,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/sem_fpn/sem_fpn.yml b/configs/sem_fpn/sem_fpn.yml
index 2644242..f00b229 100644
--- a/configs/sem_fpn/sem_fpn.yml
+++ b/configs/sem_fpn/sem_fpn.yml
@@ -1,95 +1,104 @@
 Collections:
-- Metadata:
+- Name: sem_fpn
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
-  Name: sem_fpn
+  Paper:
+    URL: https://arxiv.org/abs/1901.02446
+    Title: Panoptic Feature Pyramid Networks
+  README: configs/sem_fpn/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/facebookresearch/detectron2
 Models:
-- Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
+- Name: fpn_r50_512x1024_80k_cityscapes
   In Collection: sem_fpn
   Metadata:
     backbone: R-50
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 73.86
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 73.86
-    lr schd: 80000
     memory (GB): 2.8
-  Name: fpn_r50_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 74.52
       mIoU(ms+flip): 76.08
-    Task: Semantic Segmentation
+  Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
-- Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
+- Name: fpn_r101_512x1024_80k_cityscapes
   In Collection: sem_fpn
   Metadata:
     backbone: R-101
     crop size: (512,1024)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 97.18
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 97.18
-    lr schd: 80000
     memory (GB): 3.9
-  Name: fpn_r101_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 75.8
       mIoU(ms+flip): 77.4
-    Task: Semantic Segmentation
+  Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
-- Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
+- Name: fpn_r50_512x512_160k_ade20k
   In Collection: sem_fpn
   Metadata:
     backbone: R-50
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 17.93
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 17.93
-    lr schd: 160000
     memory (GB): 4.9
-  Name: fpn_r50_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 37.49
       mIoU(ms+flip): 39.09
-    Task: Semantic Segmentation
+  Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
-- Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
+- Name: fpn_r101_512x512_160k_ade20k
   In Collection: sem_fpn
   Metadata:
     backbone: R-101
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 24.64
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 24.64
-    lr schd: 160000
     memory (GB): 5.9
-  Name: fpn_r101_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 39.35
       mIoU(ms+flip): 40.72
-    Task: Semantic Segmentation
+  Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
diff --git a/configs/setr/README.md b/configs/setr/README.md
index 925d250..8672a28 100644
--- a/configs/setr/README.md
+++ b/configs/setr/README.md
@@ -4,6 +4,17 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/fudan-zvg/SETR">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11">Code Snippet</a>
+
+```None
+This head has two version head.
+```
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2012.15840">SETR (CVPR'2021)</a></summary>
+
 ```latex
 @article{zheng2020rethinking,
   title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers},
@@ -13,6 +24,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### ADE20K
diff --git a/configs/setr/setr.yml b/configs/setr/setr.yml
index 42e17a9..e60104d 100644
--- a/configs/setr/setr.yml
+++ b/configs/setr/setr.yml
@@ -1,87 +1,97 @@
 Collections:
-- Metadata:
+- Name: setr
+  Metadata:
     Training Data:
     - ADE20K
-  Name: setr
+  Paper:
+    URL: https://arxiv.org/abs/2012.15840
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+  README: configs/setr/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/fudan-zvg/SETR
 Models:
-- Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py
+- Name: setr_naive_512x512_160k_b16_ade20k
   In Collection: setr
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 211.86
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 211.86
-    lr schd: 160000
     memory (GB): 18.4
-  Name: setr_naive_512x512_160k_b16_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 48.28
       mIoU(ms+flip): 49.56
-    Task: Semantic Segmentation
+  Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth
-- Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py
+- Name: setr_pup_512x512_160k_b16_ade20k
   In Collection: setr
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 222.22
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 222.22
-    lr schd: 160000
     memory (GB): 19.54
-  Name: setr_pup_512x512_160k_b16_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 48.24
       mIoU(ms+flip): 49.99
-    Task: Semantic Segmentation
+  Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth
-- Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py
+- Name: setr_mla_512x512_160k_b8_ade20k
   In Collection: setr
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
     lr schd: 160000
     memory (GB): 10.96
-  Name: setr_mla_512x512_160k_b8_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.34
       mIoU(ms+flip): 49.05
-    Task: Semantic Segmentation
+  Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth
-- Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py
+- Name: setr_mla_512x512_160k_b16_ade20k
   In Collection: setr
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 190.48
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 190.48
-    lr schd: 160000
     memory (GB): 17.3
-  Name: setr_mla_512x512_160k_b16_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.54
       mIoU(ms+flip): 49.37
-    Task: Semantic Segmentation
+  Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth
diff --git a/configs/swin/README.md b/configs/swin/README.md
index 72f77f5..2d365d0 100644
--- a/configs/swin/README.md
+++ b/configs/swin/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/microsoft/Swin-Transformer">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2103.14030">Swin Transformer (arXiv'2021)</a></summary>
+
 ```latex
 @article{liu2021Swin,
   title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
diff --git a/configs/swin/swin.yml b/configs/swin/swin.yml
index 0fccb2b..5534f03 100644
--- a/configs/swin/swin.yml
+++ b/configs/swin/swin.yml
@@ -1,122 +1,131 @@
 Collections:
-- Metadata:
+- Name: swin
+  Metadata:
     Training Data:
     - ADE20K
-  Name: swin
+  Paper:
+    URL: https://arxiv.org/abs/2103.14030
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+  README: configs/swin/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/microsoft/Swin-Transformer
 Models:
-- Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   In Collection: swin
   Metadata:
     backbone: Swin-T
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 47.48
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 47.48
-    lr schd: 160000
     memory (GB): 5.02
-  Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 44.41
       mIoU(ms+flip): 45.79
-    Task: Semantic Segmentation
+  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
-- Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+- Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   In Collection: swin
   Metadata:
     backbone: Swin-S
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 67.93
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 67.93
-    lr schd: 160000
     memory (GB): 6.17
-  Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.72
       mIoU(ms+flip): 49.24
-    Task: Semantic Segmentation
+  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
-- Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   In Collection: swin
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 79.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 79.05
-    lr schd: 160000
     memory (GB): 7.61
-  Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.99
       mIoU(ms+flip): 49.57
-    Task: Semantic Segmentation
+  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
-- Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
+- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
   In Collection: swin
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
     lr schd: 160000
-  Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 50.31
       mIoU(ms+flip): 51.9
-    Task: Semantic Segmentation
+  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
-- Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
+- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
   In Collection: swin
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 82.64
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 82.64
-    lr schd: 160000
     memory (GB): 8.52
-  Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 48.35
       mIoU(ms+flip): 49.65
-    Task: Semantic Segmentation
+  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
-- Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
+- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
   In Collection: swin
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
     lr schd: 160000
-  Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 50.76
       mIoU(ms+flip): 52.4
-    Task: Semantic Segmentation
+  Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
   Weights: 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
diff --git a/configs/unet/README.md b/configs/unet/README.md
index f05bbb2..f7d4333 100644
--- a/configs/unet/README.md
+++ b/configs/unet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1505.04597">UNet (MICCAI'2016/Nat. Methods'2019)</a></summary>
+
 ```latex
 @inproceedings{ronneberger2015u,
   title={U-net: Convolutional networks for biomedical image segmentation},
@@ -15,6 +22,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### DRIVE
diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml
index 569493d..e7991f4 100644
--- a/configs/unet/unet.yml
+++ b/configs/unet/unet.yml
@@ -1,177 +1,186 @@
 Collections:
-- Metadata:
+- Name: unet
+  Metadata:
     Training Data:
     - DRIVE
     - STARE
     - CHASE_DB1
     - HRF
-  Name: unet
+  Paper:
+    URL: https://arxiv.org/abs/1505.04597
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+  README: configs/unet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+    Version: v0.17.0
+  Converted From:
+    Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
 Models:
-- Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
+- Name: fcn_unet_s5-d16_64x64_40k_drive
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
     lr schd: 40000
     memory (GB): 0.68
-  Name: fcn_unet_s5-d16_64x64_40k_drive
   Results:
+  - Task: Semantic Segmentation
     Dataset: DRIVE
     Metrics:
       mIoU: 78.67
-    Task: Semantic Segmentation
+  Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
-- Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
+- Name: pspnet_unet_s5-d16_64x64_40k_drive
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
     lr schd: 40000
     memory (GB): 0.599
-  Name: pspnet_unet_s5-d16_64x64_40k_drive
   Results:
+  - Task: Semantic Segmentation
     Dataset: DRIVE
     Metrics:
       mIoU: 78.62
-    Task: Semantic Segmentation
+  Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
-- Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
+- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
     lr schd: 40000
     memory (GB): 0.596
-  Name: deeplabv3_unet_s5-d16_64x64_40k_drive
   Results:
+  - Task: Semantic Segmentation
     Dataset: DRIVE
     Metrics:
       mIoU: 78.69
-    Task: Semantic Segmentation
+  Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
-- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
+- Name: fcn_unet_s5-d16_128x128_40k_stare
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.968
-  Name: fcn_unet_s5-d16_128x128_40k_stare
   Results:
+  - Task: Semantic Segmentation
     Dataset: STARE
     Metrics:
       mIoU: 81.02
-    Task: Semantic Segmentation
+  Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
-- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
+- Name: pspnet_unet_s5-d16_128x128_40k_stare
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.982
-  Name: pspnet_unet_s5-d16_128x128_40k_stare
   Results:
+  - Task: Semantic Segmentation
     Dataset: STARE
     Metrics:
       mIoU: 81.22
-    Task: Semantic Segmentation
+  Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
-- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
+- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.999
-  Name: deeplabv3_unet_s5-d16_128x128_40k_stare
   Results:
+  - Task: Semantic Segmentation
     Dataset: STARE
     Metrics:
       mIoU: 80.93
-    Task: Semantic Segmentation
+  Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
-- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
+- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.968
-  Name: fcn_unet_s5-d16_128x128_40k_chase_db1
   Results:
+  - Task: Semantic Segmentation
     Dataset: CHASE_DB1
     Metrics:
       mIoU: 80.24
-    Task: Semantic Segmentation
+  Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
-- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
+- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.982
-  Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
   Results:
+  - Task: Semantic Segmentation
     Dataset: CHASE_DB1
     Metrics:
       mIoU: 80.36
-    Task: Semantic Segmentation
+  Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
-- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
+- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
     lr schd: 40000
     memory (GB): 0.999
-  Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
   Results:
+  - Task: Semantic Segmentation
     Dataset: CHASE_DB1
     Metrics:
       mIoU: 80.47
-    Task: Semantic Segmentation
+  Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
-- Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
+- Name: fcn_unet_s5-d16_256x256_40k_hrf
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
     lr schd: 40000
     memory (GB): 2.525
-  Name: fcn_unet_s5-d16_256x256_40k_hrf
   Results:
+  - Task: Semantic Segmentation
     Dataset: HRF
     Metrics:
       mIoU: 79.45
-    Task: Semantic Segmentation
+  Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
-- Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
+- Name: pspnet_unet_s5-d16_256x256_40k_hrf
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
     lr schd: 40000
     memory (GB): 2.588
-  Name: pspnet_unet_s5-d16_256x256_40k_hrf
   Results:
+  - Task: Semantic Segmentation
     Dataset: HRF
     Metrics:
       mIoU: 80.07
-    Task: Semantic Segmentation
+  Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
-- Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
+- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
   In Collection: unet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
     lr schd: 40000
     memory (GB): 2.604
-  Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
   Results:
+  - Task: Semantic Segmentation
     Dataset: HRF
     Metrics:
       mIoU: 80.21
-    Task: Semantic Segmentation
+  Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
diff --git a/configs/upernet/README.md b/configs/upernet/README.md
index 312004a..5d3f85b 100644
--- a/configs/upernet/README.md
+++ b/configs/upernet/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/CSAILVision/unifiedparsing">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/pdf/1807.10221.pdf">UPerNet (ECCV'2018)</a></summary>
+
 ```latex
 @inproceedings{xiao2018unified,
   title={Unified perceptual parsing for scene understanding},
@@ -14,6 +21,8 @@
 }
 ```
 
+</details>
+
 ## Results and models
 
 ### Cityscapes
diff --git a/configs/upernet/upernet.yml b/configs/upernet/upernet.yml
index 91503cb..a5a5c85 100644
--- a/configs/upernet/upernet.yml
+++ b/configs/upernet/upernet.yml
@@ -1,296 +1,305 @@
 Collections:
-- Metadata:
+- Name: upernet
+  Metadata:
     Training Data:
     - Cityscapes
     - ADE20K
     - Pascal VOC 2012 + Aug
-  Name: upernet
+  Paper:
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+    Title: Unified Perceptual Parsing for Scene Understanding
+  README: configs/upernet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/CSAILVision/unifiedparsing
 Models:
-- Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
+- Name: upernet_r50_512x1024_40k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 235.29
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 235.29
-    lr schd: 40000
     memory (GB): 6.4
-  Name: upernet_r50_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.1
       mIoU(ms+flip): 78.37
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
-- Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
+- Name: upernet_r101_512x1024_40k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,1024)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 263.85
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,1024)
-      value: 263.85
-    lr schd: 40000
     memory (GB): 7.4
-  Name: upernet_r101_512x1024_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.69
       mIoU(ms+flip): 80.11
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
-- Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
+- Name: upernet_r50_769x769_40k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 568.18
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 568.18
-    lr schd: 40000
     memory (GB): 7.2
-  Name: upernet_r50_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 77.98
       mIoU(ms+flip): 79.7
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
-- Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
+- Name: upernet_r101_769x769_40k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (769,769)
+    lr schd: 40000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 641.03
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (769,769)
-      value: 641.03
-    lr schd: 40000
     memory (GB): 8.4
-  Name: upernet_r101_769x769_40k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.03
       mIoU(ms+flip): 80.77
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
-- Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
+- Name: upernet_r50_512x1024_80k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,1024)
     lr schd: 80000
-  Name: upernet_r50_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 78.19
       mIoU(ms+flip): 79.19
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
-- Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
+- Name: upernet_r101_512x1024_80k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,1024)
     lr schd: 80000
-  Name: upernet_r101_512x1024_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.4
       mIoU(ms+flip): 80.46
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
-- Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
+- Name: upernet_r50_769x769_80k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (769,769)
     lr schd: 80000
-  Name: upernet_r50_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 79.39
       mIoU(ms+flip): 80.92
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
-- Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
+- Name: upernet_r101_769x769_80k_cityscapes
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (769,769)
     lr schd: 80000
-  Name: upernet_r101_769x769_80k_cityscapes
   Results:
+  - Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 80.1
       mIoU(ms+flip): 81.49
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
-- Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
+- Name: upernet_r50_512x512_80k_ade20k
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 42.74
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 42.74
-    lr schd: 80000
     memory (GB): 8.1
-  Name: upernet_r50_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 40.7
       mIoU(ms+flip): 41.81
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
-- Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
+- Name: upernet_r101_512x512_80k_ade20k
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 49.16
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 49.16
-    lr schd: 80000
     memory (GB): 9.1
-  Name: upernet_r101_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.91
       mIoU(ms+flip): 43.96
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
-- Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
+- Name: upernet_r50_512x512_160k_ade20k
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,512)
     lr schd: 160000
-  Name: upernet_r50_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.05
       mIoU(ms+flip): 42.78
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
-- Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
+- Name: upernet_r101_512x512_160k_ade20k
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,512)
     lr schd: 160000
-  Name: upernet_r101_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.82
       mIoU(ms+flip): 44.85
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
-- Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
+- Name: upernet_r50_512x512_20k_voc12aug
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 43.16
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 43.16
-    lr schd: 20000
     memory (GB): 6.4
-  Name: upernet_r50_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 74.82
       mIoU(ms+flip): 76.35
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
-- Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
+- Name: upernet_r101_512x512_20k_voc12aug
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,512)
+    lr schd: 20000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 50.05
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 50.05
-    lr schd: 20000
     memory (GB): 7.5
-  Name: upernet_r101_512x512_20k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.1
       mIoU(ms+flip): 78.29
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
-- Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
+- Name: upernet_r50_512x512_40k_voc12aug
   In Collection: upernet
   Metadata:
     backbone: R-50
     crop size: (512,512)
     lr schd: 40000
-  Name: upernet_r50_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 75.92
       mIoU(ms+flip): 77.44
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
-- Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
+- Name: upernet_r101_512x512_40k_voc12aug
   In Collection: upernet
   Metadata:
     backbone: R-101
     crop size: (512,512)
     lr schd: 40000
-  Name: upernet_r101_512x512_40k_voc12aug
   Results:
+  - Task: Semantic Segmentation
     Dataset: Pascal VOC 2012 + Aug
     Metrics:
       mIoU: 77.43
       mIoU(ms+flip): 78.56
-    Task: Semantic Segmentation
+  Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
diff --git a/configs/vit/README.md b/configs/vit/README.md
index 3218aff..655b8b5 100644
--- a/configs/vit/README.md
+++ b/configs/vit/README.md
@@ -4,6 +4,13 @@
 
 <!-- [ALGORITHM] -->
 
+<a href="https://github.com/google-research/vision_transformer">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/pdf/2010.11929.pdf">Vision Transformer (ICLR'2021)</a></summary>
+
 ```latex
 @article{dosoViTskiy2020,
   title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
@@ -13,6 +20,8 @@
 }
 ```
 
+</details>
+
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
diff --git a/configs/vit/vit.yml b/configs/vit/vit.yml
index 0d52634..5692a64 100644
--- a/configs/vit/vit.yml
+++ b/configs/vit/vit.yml
@@ -1,248 +1,257 @@
 Collections:
-- Metadata:
+- Name: vit
+  Metadata:
     Training Data:
     - ADE20K
-  Name: vit
+  Paper:
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+    Title: Vision Transformer
+  README: configs/vit/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/google-research/vision_transformer
 Models:
-- Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
+- Name: upernet_vit-b16_mln_512x512_80k_ade20k
   In Collection: vit
   Metadata:
     backbone: ViT-B + MLN
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 144.09
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 144.09
-    lr schd: 80000
     memory (GB): 9.2
-  Name: upernet_vit-b16_mln_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.71
       mIoU(ms+flip): 49.51
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
-- Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
+- Name: upernet_vit-b16_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: ViT-B + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 131.93
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 131.93
-    lr schd: 160000
     memory (GB): 9.2
-  Name: upernet_vit-b16_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 46.75
       mIoU(ms+flip): 48.46
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
-- Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
+- Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: ViT-B + LN + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 146.63
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 146.63
-    lr schd: 160000
     memory (GB): 9.21
-  Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 47.73
       mIoU(ms+flip): 49.95
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
-- Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
+- Name: upernet_deit-s16_512x512_80k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-S
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 33.5
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 33.5
-    lr schd: 80000
     memory (GB): 4.68
-  Name: upernet_deit-s16_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.96
       mIoU(ms+flip): 43.79
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
-- Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
+- Name: upernet_deit-s16_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-S
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 34.26
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 34.26
-    lr schd: 160000
     memory (GB): 4.68
-  Name: upernet_deit-s16_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 42.87
       mIoU(ms+flip): 43.79
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
-- Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
+- Name: upernet_deit-s16_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-S + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 89.45
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 89.45
-    lr schd: 160000
     memory (GB): 5.69
-  Name: upernet_deit-s16_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.82
       mIoU(ms+flip): 45.07
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
-- Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
+- Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-S + LN + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 80.71
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 80.71
-    lr schd: 160000
     memory (GB): 5.69
-  Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 43.52
       mIoU(ms+flip): 45.01
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
-- Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
+- Name: upernet_deit-b16_512x512_80k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-B
     crop size: (512,512)
+    lr schd: 80000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 103.2
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 103.2
-    lr schd: 80000
     memory (GB): 7.75
-  Name: upernet_deit-b16_512x512_80k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.24
       mIoU(ms+flip): 46.73
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
-- Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
+- Name: upernet_deit-b16_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-B
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 96.25
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 96.25
-    lr schd: 160000
     memory (GB): 7.75
-  Name: upernet_deit-b16_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.36
       mIoU(ms+flip): 47.16
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
-- Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
+- Name: upernet_deit-b16_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-B + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 128.53
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 128.53
-    lr schd: 160000
     memory (GB): 9.21
-  Name: upernet_deit-b16_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.46
       mIoU(ms+flip): 47.16
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
-- Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
+- Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
   In Collection: vit
   Metadata:
     backbone: DeiT-B + LN + MLN
     crop size: (512,512)
+    lr schd: 160000
     inference time (ms/im):
-    - backend: PyTorch
-      batch size: 1
+    - value: 129.03
       hardware: V100
+      backend: PyTorch
+      batch size: 1
       mode: FP32
       resolution: (512,512)
-      value: 129.03
-    lr schd: 160000
     memory (GB): 9.21
-  Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
   Results:
+  - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
       mIoU: 45.37
       mIoU(ms+flip): 47.23
-    Task: Semantic Segmentation
+  Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth