From ed954fffd9abb5c5151a17938eeba295e85f5094 Mon Sep 17 00:00:00 2001
From: MengzhangLI <mcmong@pku.edu.cn>
Date: Wed, 23 Feb 2022 18:00:28 +0800
Subject: [PATCH] [Fix] Update correct  `In Collection` in metafile of each
 configs. (#1239)

* change md2yml file

* update metafile

* update twins In Collection automatically

* fix twins metafile

* fix twins metafile

* all metafile use value of Method

* update collect name

* update collect name

* fix some typo

* fix FCN D6

* change JPU to FastFCN

* fix some typos in DNLNet, NonLocalNet, SETR, Segmenter, STDC, FastSCNN

* fix typo in stdc

* fix typo in DNLNet and UNet

* fix NonLocalNet typo
---
 .dev/md2yml.py                          |  21 ++++-
 configs/ann/ann.yml                     |  34 ++++----
 configs/apcnet/apcnet.yml               |  26 +++---
 configs/bisenetv1/README.md             |   2 +-
 configs/bisenetv1/bisenetv1.yml         |  24 +++---
 configs/bisenetv2/bisenetv2.yml         |  10 +--
 configs/ccnet/ccnet.yml                 |  34 ++++----
 configs/cgnet/cgnet.yml                 |   6 +-
 configs/danet/danet.yml                 |  34 ++++----
 configs/deeplabv3/deeplabv3.yml         |  82 +++++++++----------
 configs/deeplabv3plus/deeplabv3plus.yml |  86 ++++++++++----------
 configs/dmnet/dmnet.yml                 |  26 +++---
 configs/dnlnet/README.md                |  24 +++---
 configs/dnlnet/dnlnet.yml               |  26 +++---
 configs/dpt/dpt.yml                     |   4 +-
 configs/emanet/emanet.yml               |  10 +--
 configs/encnet/encnet.yml               |  26 +++---
 configs/erfnet/README.md                |   2 +-
 configs/erfnet/erfnet.yml               |   4 +-
 configs/fastfcn/README.md               |  24 +++---
 configs/fastfcn/fastfcn.yml             |  26 +++---
 configs/fastscnn/README.md              |   2 +-
 configs/fastscnn/fastscnn.yml           |   6 +-
 configs/fcn/README.md                   |  25 +++---
 configs/fcn/fcn.yml                     |  84 +++++++++----------
 configs/gcnet/gcnet.yml                 |  34 ++++----
 configs/hrnet/hrnet.yml                 |  96 +++++++++-------------
 configs/icnet/icnet.yml                 |  26 +++---
 configs/isanet/isanet.yml               |  34 ++++----
 configs/mobilenet_v2/mobilenet_v2.yml   |  31 ++-----
 configs/mobilenet_v3/mobilenet_v3.yml   |  22 +----
 configs/nonlocal_net/README.md          |  32 ++++----
 configs/nonlocal_net/nonlocal_net.yml   |  34 ++++----
 configs/ocrnet/ocrnet.yml               |  50 ++++++------
 configs/point_rend/point_rend.yml       |  10 +--
 configs/psanet/psanet.yml               |  34 ++++----
 configs/pspnet/pspnet.yml               | 102 ++++++++++++------------
 configs/resnest/resnest.yml             |  31 ++-----
 configs/segformer/segformer.yml         |  28 +++----
 configs/segmenter/README.md             |  10 +--
 configs/segmenter/segmenter.yml         |  12 +--
 configs/sem_fpn/sem_fpn.yml             |  10 +--
 configs/setr/README.md                  |  14 ++--
 configs/setr/setr.yml                   |  16 ++--
 configs/stdc/README.md                  |   8 +-
 configs/stdc/stdc.yml                   |  10 +--
 configs/swin/swin.yml                   |  26 ++----
 configs/twins/twins.yml                 |  38 +++------
 configs/unet/README.md                  |  50 ++++++------
 configs/unet/unet.yml                   |  52 ++++++------
 configs/upernet/upernet.yml             |  34 ++++----
 configs/vit/vit.yml                     |  36 +++------
 52 files changed, 718 insertions(+), 810 deletions(-)

diff --git a/.dev/md2yml.py b/.dev/md2yml.py
index 4c2e129..7fce17a 100755
--- a/.dev/md2yml.py
+++ b/.dev/md2yml.py
@@ -81,6 +81,13 @@ def parse_md(md_file):
     code_version = None
     repo_url = None
 
+    # To avoid re-counting number of backbone model in OpenMMLab,
+    # if certain model in configs folder is backbone whose name is already
+    # recorded in MMClassification, then the `COLLECTION` dict of this model
+    # in MMSegmentation should be deleted, and `In Collection` in `Models`
+    # should be set with head or neck of this config file.
+    is_backbone = None
+
     with open(md_file, 'r') as md:
         lines = md.readlines()
         i = 0
@@ -117,9 +124,13 @@ def parse_md(md_file):
                 datasets.append(line[4:])
                 current_dataset = line[4:]
                 i += 2
+            elif line[:15] == '<!-- [BACKBONE]':
+                is_backbone = True
+                i += 1
             elif line[0] == '|' and (
                     i + 1) < len(lines) and lines[i + 1][:3] == '| -':
                 cols = [col.strip() for col in line.split('|')]
+                method_id = cols.index('Method')
                 backbone_id = cols.index('Backbone')
                 crop_size_id = cols.index('Crop Size')
                 lr_schd_id = cols.index('Lr schd')
@@ -155,11 +166,12 @@ def parse_md(md_file):
                         mem_id] != '' else -1
                     crop_size = els[crop_size_id].split('x')
                     assert len(crop_size) == 2
+                    method = els[method_id].split()[0].split('-')[-1]
                     model = {
                         'Name':
                         model_name,
                         'In Collection':
-                        collection_name,
+                        method,
                         'Metadata': {
                             'backbone': els[backbone_id],
                             'crop size': f'({crop_size[0]},{crop_size[1]})',
@@ -213,6 +225,7 @@ def parse_md(md_file):
     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['Name'] = method
     collection['Metadata']['Training Data'] = datasets
     collection['Code']['URL'] = code_url
     collection['Code']['Version'] = code_version
@@ -232,8 +245,10 @@ def parse_md(md_file):
                     collection.pop(check_key)
                 else:
                     collection[check_key].pop(key)
-
-    result = {'Collections': [collection], 'Models': models}
+    if is_backbone:
+        result = {'Models': models}
+    else:
+        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/configs/ann/ann.yml b/configs/ann/ann.yml
index 9f41407..ff6bea6 100644
--- a/configs/ann/ann.yml
+++ b/configs/ann/ann.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: ann
+- Name: ANN
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/MendelXu/ANN
 Models:
 - Name: ann_r50-d8_512x1024_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: ann_r101-d8_512x1024_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: ann_r50-d8_769x769_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: ann_r101-d8_769x769_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: ann_r50-d8_512x1024_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   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
 - Name: ann_r101-d8_512x1024_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: ann_r50-d8_769x769_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: ann_r101-d8_769x769_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: ann_r50-d8_512x512_80k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   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
 - Name: ann_r101-d8_512x512_80k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   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
 - Name: ann_r50-d8_512x512_160k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   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
 - Name: ann_r101-d8_512x512_160k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   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
 - Name: ann_r50-d8_512x512_20k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   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
 - Name: ann_r101-d8_512x512_20k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   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
 - Name: ann_r50-d8_512x512_40k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: ann_r101-d8_512x512_40k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/apcnet/apcnet.yml b/configs/apcnet/apcnet.yml
index 2f84aad..7a453a3 100644
--- a/configs/apcnet/apcnet.yml
+++ b/configs/apcnet/apcnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: apcnet
+- Name: APCNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/Junjun2016/APCNet
 Models:
 - Name: apcnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: apcnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   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
 - Name: apcnet_r50-d8_769x769_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -81,7 +81,7 @@ Models:
   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
 - Name: apcnet_r101-d8_769x769_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -103,7 +103,7 @@ Models:
   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
 - Name: apcnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -117,7 +117,7 @@ Models:
   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
 - Name: apcnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -131,7 +131,7 @@ Models:
   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
 - Name: apcnet_r50-d8_769x769_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -145,7 +145,7 @@ Models:
   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
 - Name: apcnet_r101-d8_769x769_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -159,7 +159,7 @@ Models:
   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
 - Name: apcnet_r50-d8_512x512_80k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -181,7 +181,7 @@ Models:
   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
 - Name: apcnet_r101-d8_512x512_80k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -203,7 +203,7 @@ Models:
   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
 - Name: apcnet_r50-d8_512x512_160k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -217,7 +217,7 @@ Models:
   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
 - Name: apcnet_r101-d8_512x512_160k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/bisenetv1/README.md b/configs/bisenetv1/README.md
index 7a9e5fa..75ac37c 100644
--- a/configs/bisenetv1/README.md
+++ b/configs/bisenetv1/README.md
@@ -53,7 +53,7 @@ Semantic segmentation requires both rich spatial information and sizeable recept
 | BiSeNetV1| R-18-D32 | 512x512  | 160000 | 6.33 | 74.24 | 28.55 | 29.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json) |
 | BiSeNetV1 (No Pretrain) | R-50-D32 | 512x512  | 160000 | - | - | 29.82 | 30.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json) |
 | BiSeNetV1 | R-50-D32 | 512x512  | 160000 | 9.28 | 32.60 | 34.88 | 35.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json) |
-| BiSeNetV1(No Pretrain) | R-101-D32 | 512x512  | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) |
+| BiSeNetV1 (No Pretrain) | R-101-D32 | 512x512  | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) |
 | BiSeNetV1 | R-101-D32 | 512x512  | 160000 | 10.36 | 25.25 | 37.38 | 37.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) |
 
 Note:
diff --git a/configs/bisenetv1/bisenetv1.yml b/configs/bisenetv1/bisenetv1.yml
index acde4ff..61f264b 100644
--- a/configs/bisenetv1/bisenetv1.yml
+++ b/configs/bisenetv1/bisenetv1.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: bisenetv1
+- Name: BiSeNetV1
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
 Models:
 - Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-18-D32
     crop size: (1024,1024)
@@ -37,7 +37,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
 - Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-18-D32
     crop size: (1024,1024)
@@ -59,7 +59,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
 - Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-18-D32
     crop size: (1024,1024)
@@ -81,7 +81,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
 - Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-50-D32
     crop size: (1024,1024)
@@ -103,7 +103,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
 - Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-50-D32
     crop size: (1024,1024)
@@ -125,7 +125,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
 - Name: bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-18-D32
     crop size: (512,512)
@@ -139,7 +139,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth
 - Name: bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-18-D32
     crop size: (512,512)
@@ -161,7 +161,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth
 - Name: bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-50-D32
     crop size: (512,512)
@@ -175,7 +175,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth
 - Name: bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-50-D32
     crop size: (512,512)
@@ -197,7 +197,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth
 - Name: bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-101-D32
     crop size: (512,512)
@@ -211,7 +211,7 @@ Models:
   Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth
 - Name: bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
-  In Collection: bisenetv1
+  In Collection: BiSeNetV1
   Metadata:
     backbone: R-101-D32
     crop size: (512,512)
diff --git a/configs/bisenetv2/bisenetv2.yml b/configs/bisenetv2/bisenetv2.yml
index 82bab7e..455fa6c 100644
--- a/configs/bisenetv2/bisenetv2.yml
+++ b/configs/bisenetv2/bisenetv2.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: bisenetv2
+- Name: BiSeNetV2
   Metadata:
     Training Data:
     - Cityscapes
@@ -13,7 +13,7 @@ Collections:
     Version: v0.18.0
 Models:
 - Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv2
+  In Collection: BiSeNetV2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
@@ -35,7 +35,7 @@ Models:
   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
 - Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv2
+  In Collection: BiSeNetV2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
@@ -50,7 +50,7 @@ Models:
   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
 - Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
-  In Collection: bisenetv2
+  In Collection: BiSeNetV2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
@@ -65,7 +65,7 @@ Models:
   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
 - Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
-  In Collection: bisenetv2
+  In Collection: BiSeNetV2
   Metadata:
     backbone: BiSeNetV2
     crop size: (1024,1024)
diff --git a/configs/ccnet/ccnet.yml b/configs/ccnet/ccnet.yml
index 5e5d955..b264f2e 100644
--- a/configs/ccnet/ccnet.yml
+++ b/configs/ccnet/ccnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: ccnet
+- Name: CCNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/speedinghzl/CCNet
 Models:
 - Name: ccnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: ccnet_r50-d8_769x769_40k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: ccnet_r101-d8_769x769_40k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: ccnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: ccnet_r50-d8_769x769_80k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: ccnet_r101-d8_769x769_80k_cityscapes
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: ccnet_r50-d8_512x512_80k_ade20k
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x512_80k_ade20k
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   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
 - Name: ccnet_r50-d8_512x512_160k_ade20k
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x512_160k_ade20k
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   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
 - Name: ccnet_r50-d8_512x512_20k_voc12aug
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x512_20k_voc12aug
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   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
 - Name: ccnet_r50-d8_512x512_40k_voc12aug
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: ccnet_r101-d8_512x512_40k_voc12aug
-  In Collection: ccnet
+  In Collection: CCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/cgnet/cgnet.yml b/configs/cgnet/cgnet.yml
index b1506e0..bcd6d89 100644
--- a/configs/cgnet/cgnet.yml
+++ b/configs/cgnet/cgnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: cgnet
+- Name: CGNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/wutianyiRosun/CGNet
 Models:
 - Name: cgnet_680x680_60k_cityscapes
-  In Collection: cgnet
+  In Collection: CGNet
   Metadata:
     backbone: M3N21
     crop size: (680,680)
@@ -36,7 +36,7 @@ Models:
   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
 - Name: cgnet_512x1024_60k_cityscapes
-  In Collection: cgnet
+  In Collection: CGNet
   Metadata:
     backbone: M3N21
     crop size: (512,1024)
diff --git a/configs/danet/danet.yml b/configs/danet/danet.yml
index ac53939..ca2d6ff 100644
--- a/configs/danet/danet.yml
+++ b/configs/danet/danet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: danet
+- Name: DANet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/junfu1115/DANet/
 Models:
 - Name: danet_r50-d8_512x1024_40k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: danet_r101-d8_512x1024_40k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -58,7 +58,7 @@ Models:
   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
 - Name: danet_r50-d8_769x769_40k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -80,7 +80,7 @@ Models:
   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
 - Name: danet_r101-d8_769x769_40k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -102,7 +102,7 @@ Models:
   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
 - Name: danet_r50-d8_512x1024_80k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -115,7 +115,7 @@ Models:
   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
 - Name: danet_r101-d8_512x1024_80k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -128,7 +128,7 @@ Models:
   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
 - Name: danet_r50-d8_769x769_80k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -142,7 +142,7 @@ Models:
   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
 - Name: danet_r101-d8_769x769_80k_cityscapes
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -156,7 +156,7 @@ Models:
   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
 - Name: danet_r50-d8_512x512_80k_ade20k
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -178,7 +178,7 @@ Models:
   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
 - Name: danet_r101-d8_512x512_80k_ade20k
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -200,7 +200,7 @@ Models:
   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
 - Name: danet_r50-d8_512x512_160k_ade20k
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -214,7 +214,7 @@ Models:
   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
 - Name: danet_r101-d8_512x512_160k_ade20k
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -228,7 +228,7 @@ Models:
   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
 - Name: danet_r50-d8_512x512_20k_voc12aug
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -250,7 +250,7 @@ Models:
   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
 - Name: danet_r101-d8_512x512_20k_voc12aug
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -272,7 +272,7 @@ Models:
   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
 - Name: danet_r50-d8_512x512_40k_voc12aug
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -286,7 +286,7 @@ Models:
   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
 - Name: danet_r101-d8_512x512_40k_voc12aug
-  In Collection: danet
+  In Collection: DANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/deeplabv3/deeplabv3.yml b/configs/deeplabv3/deeplabv3.yml
index e8bdfa6..559af4f 100644
--- a/configs/deeplabv3/deeplabv3.yml
+++ b/configs/deeplabv3/deeplabv3.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: deeplabv3
+- Name: DeepLabV3
   Metadata:
     Training Data:
     - Cityscapes
@@ -20,7 +20,7 @@ Collections:
     Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
 - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -42,7 +42,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -64,7 +64,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_769x769_40k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -86,7 +86,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_769x769_40k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -108,7 +108,7 @@ Models:
   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
 - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
@@ -130,7 +130,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -144,7 +144,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -158,7 +158,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -179,7 +179,7 @@ Models:
   Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth
 - Name: deeplabv3_r18-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
@@ -201,7 +201,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -215,7 +215,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -229,7 +229,7 @@ Models:
   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
 - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
@@ -243,7 +243,7 @@ Models:
   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
 - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
@@ -265,7 +265,7 @@ Models:
   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
 - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
@@ -287,7 +287,7 @@ Models:
   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
 - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
@@ -309,7 +309,7 @@ Models:
   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
 - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
@@ -331,7 +331,7 @@ Models:
   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
 - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
@@ -353,7 +353,7 @@ Models:
   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
 - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
@@ -375,7 +375,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_80k_ade20k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -397,7 +397,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_80k_ade20k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -419,7 +419,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_160k_ade20k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -433,7 +433,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_160k_ade20k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -447,7 +447,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_20k_voc12aug
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -469,7 +469,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_20k_voc12aug
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -491,7 +491,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_40k_voc12aug
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -505,7 +505,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_40k_voc12aug
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -519,7 +519,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_480x480_40k_pascal_context
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -541,7 +541,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_480x480_80k_pascal_context
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -555,7 +555,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -569,7 +569,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -583,7 +583,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -605,7 +605,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -627,7 +627,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -641,7 +641,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -655,7 +655,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -677,7 +677,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -699,7 +699,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -713,7 +713,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -727,7 +727,7 @@ Models:
   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
 - Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -741,7 +741,7 @@ Models:
   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
 - Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k
-  In Collection: deeplabv3
+  In Collection: DeepLabV3
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml
index a587216..c4521ee 100644
--- a/configs/deeplabv3plus/deeplabv3plus.yml
+++ b/configs/deeplabv3plus/deeplabv3plus.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: deeplabv3plus
+- Name: DeepLabV3+
   Metadata:
     Training Data:
     - Cityscapes
@@ -22,7 +22,7 @@ Collections:
     Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
 - Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -44,7 +44,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -66,7 +66,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -88,7 +88,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -110,7 +110,7 @@ Models:
   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
 - Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -181,7 +181,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth
 - Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
@@ -203,7 +203,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -217,7 +217,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -231,7 +231,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
@@ -253,7 +253,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D16-MG124
     crop size: (512,1024)
@@ -268,7 +268,7 @@ Models:
   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
 - Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
@@ -312,7 +312,7 @@ Models:
   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
 - Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
@@ -334,7 +334,7 @@ Models:
   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
 - Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
@@ -356,7 +356,7 @@ Models:
   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
 - Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
@@ -378,7 +378,7 @@ Models:
   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
 - Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
@@ -400,7 +400,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -422,7 +422,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -444,7 +444,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -458,7 +458,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -472,7 +472,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -494,7 +494,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -516,7 +516,7 @@ Models:
   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
 - Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -530,7 +530,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -544,7 +544,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -565,7 +565,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -579,7 +579,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -593,7 +593,7 @@ Models:
   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
 - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -607,7 +607,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
 - Name: deeplabv3plus_r18-d8_512x512_80k_loveda
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -629,7 +629,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
 - Name: deeplabv3plus_r50-d8_512x512_80k_loveda
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -651,7 +651,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
 - Name: deeplabv3plus_r101-d8_512x512_80k_loveda
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -673,7 +673,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
 - Name: deeplabv3plus_r18-d8_512x512_80k_potsdam
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -695,7 +695,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
 - Name: deeplabv3plus_r50-d8_512x512_80k_potsdam
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -717,7 +717,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
 - Name: deeplabv3plus_r101-d8_512x512_80k_potsdam
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -739,7 +739,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
 - Name: deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -761,7 +761,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth
 - Name: deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -783,7 +783,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth
 - Name: deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -805,7 +805,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
 - Name: deeplabv3plus_r18-d8_4x4_896x896_80k_isaid
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-18-D8
     crop size: (896,896)
@@ -827,7 +827,7 @@ Models:
   Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
 - Name: deeplabv3plus_r50-d8_4x4_896x896_80k_isaid
-  In Collection: deeplabv3plus
+  In Collection: DeepLabV3+
   Metadata:
     backbone: R-50-D8
     crop size: (896,896)
diff --git a/configs/dmnet/dmnet.yml b/configs/dmnet/dmnet.yml
index a224033..1fab2dc 100644
--- a/configs/dmnet/dmnet.yml
+++ b/configs/dmnet/dmnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: dmnet
+- Name: DMNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/Junjun2016/DMNet
 Models:
 - Name: dmnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: dmnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   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
 - Name: dmnet_r50-d8_769x769_40k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -81,7 +81,7 @@ Models:
   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
 - Name: dmnet_r101-d8_769x769_40k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -103,7 +103,7 @@ Models:
   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
 - Name: dmnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -117,7 +117,7 @@ Models:
   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
 - Name: dmnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -131,7 +131,7 @@ Models:
   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
 - Name: dmnet_r50-d8_769x769_80k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -145,7 +145,7 @@ Models:
   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
 - Name: dmnet_r101-d8_769x769_80k_cityscapes
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -159,7 +159,7 @@ Models:
   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
 - Name: dmnet_r50-d8_512x512_80k_ade20k
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -181,7 +181,7 @@ Models:
   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
 - Name: dmnet_r101-d8_512x512_80k_ade20k
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -203,7 +203,7 @@ Models:
   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
 - Name: dmnet_r50-d8_512x512_160k_ade20k
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -217,7 +217,7 @@ Models:
   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
 - Name: dmnet_r101-d8_512x512_160k_ade20k
-  In Collection: dmnet
+  In Collection: DMNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/dnlnet/README.md b/configs/dnlnet/README.md
index ff33511..d36f099 100644
--- a/configs/dnlnet/README.md
+++ b/configs/dnlnet/README.md
@@ -42,20 +42,20 @@ This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                         |
 | ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DNL    | R-50-D8  | 512x1024  |   40000 |      7.3 | 2.56           | 78.61 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json)     |
-| DNL    | R-101-D8 | 512x1024  |   40000 |     10.9 | 1.96           | 78.31 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) |
-| DNL    | R-50-D8  | 769x769   |   40000 |      9.2 | 1.50           | 78.44 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json)         |
-| DNL    | R-101-D8 | 769x769   |   40000 |     12.6 | 1.02           | 76.39 | 77.77         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json)     |
-| DNL    | R-50-D8  | 512x1024  |   80000 |        - | -              | 79.33 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json)     |
-| DNL    | R-101-D8 | 512x1024  |   80000 |        - | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) |
-| DNL    | R-50-D8  | 769x769   |   80000 |        - | -              | 79.36 | 80.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json)         |
-| DNL    | R-101-D8 | 769x769   |   80000 |        - | -              | 79.41 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json)     |
+| DNLNet    | R-50-D8  | 512x1024  |   40000 |      7.3 | 2.56           | 78.61 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json)     |
+| DNLNet    | R-101-D8 | 512x1024  |   40000 |     10.9 | 1.96           | 78.31 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) |
+| DNLNet    | R-50-D8  | 769x769   |   40000 |      9.2 | 1.50           | 78.44 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json)         |
+| DNLNet    | R-101-D8 | 769x769   |   40000 |     12.6 | 1.02           | 76.39 | 77.77         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json)     |
+| DNLNet    | R-50-D8  | 512x1024  |   80000 |        - | -              | 79.33 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json)     |
+| DNLNet    | R-101-D8 | 512x1024  |   80000 |        - | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) |
+| DNLNet    | R-50-D8  | 769x769   |   80000 |        - | -              | 79.36 | 80.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json)         |
+| DNLNet    | R-101-D8 | 769x769   |   80000 |        - | -              | 79.41 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json)     |
 
 ### ADE20K
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                               | download                                                                                                                                                                                                                                                                                                                         |
 | ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DNL    | R-50-D8  | 512x512   |   80000 |      8.8 | 20.66          | 41.76 | 42.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json)         |
-| DNL    | R-101-D8 | 512x512   |   80000 |     12.8 | 12.54          | 43.76 | 44.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json)     |
-| DNL    | R-50-D8  | 512x512   |  160000 |        - | -              | 41.87 | 43.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json)     |
-| DNL    | R-101-D8 | 512x512   |  160000 |        - | -              | 44.25 | 45.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) |
+| DNLNet    | R-50-D8  | 512x512   |   80000 |      8.8 | 20.66          | 41.76 | 42.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json)         |
+| DNLNet    | R-101-D8 | 512x512   |   80000 |     12.8 | 12.54          | 43.76 | 44.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json)     |
+| DNLNet    | R-50-D8  | 512x512   |  160000 |        - | -              | 41.87 | 43.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json)     |
+| DNLNet    | R-101-D8 | 512x512   |  160000 |        - | -              | 44.25 | 45.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) |
diff --git a/configs/dnlnet/dnlnet.yml b/configs/dnlnet/dnlnet.yml
index 81e9fca..8ee7b54 100644
--- a/configs/dnlnet/dnlnet.yml
+++ b/configs/dnlnet/dnlnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: dnlnet
+- Name: DNLNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/yinmh17/DNL-Semantic-Segmentation
 Models:
 - Name: dnl_r50-d8_512x1024_40k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -36,7 +36,7 @@ Models:
   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
 - Name: dnl_r101-d8_512x1024_40k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -57,7 +57,7 @@ Models:
   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
 - Name: dnl_r50-d8_769x769_40k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -79,7 +79,7 @@ Models:
   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
 - Name: dnl_r101-d8_769x769_40k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -101,7 +101,7 @@ Models:
   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
 - Name: dnl_r50-d8_512x1024_80k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -114,7 +114,7 @@ Models:
   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
 - Name: dnl_r101-d8_512x1024_80k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -127,7 +127,7 @@ Models:
   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
 - Name: dnl_r50-d8_769x769_80k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -141,7 +141,7 @@ Models:
   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
 - Name: dnl_r101-d8_769x769_80k_cityscapes
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -155,7 +155,7 @@ Models:
   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
 - Name: dnl_r50-d8_512x512_80k_ade20k
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -177,7 +177,7 @@ Models:
   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
 - Name: dnl_r101-d8_512x512_80k_ade20k
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -199,7 +199,7 @@ Models:
   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
 - Name: dnl_r50-d8_512x512_160k_ade20k
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -213,7 +213,7 @@ Models:
   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
 - Name: dnl_r101-d8_512x512_160k_ade20k
-  In Collection: dnlnet
+  In Collection: DNLNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/dpt/dpt.yml b/configs/dpt/dpt.yml
index f1cf942..a4f9c65 100644
--- a/configs/dpt/dpt.yml
+++ b/configs/dpt/dpt.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: dpt
+- Name: DPT
   Metadata:
     Training Data:
     - ADE20K
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/isl-org/DPT
 Models:
 - Name: dpt_vit-b16_512x512_160k_ade20k
-  In Collection: dpt
+  In Collection: DPT
   Metadata:
     backbone: ViT-B
     crop size: (512,512)
diff --git a/configs/emanet/emanet.yml b/configs/emanet/emanet.yml
index 173baa0..22ebcdb 100644
--- a/configs/emanet/emanet.yml
+++ b/configs/emanet/emanet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: emanet
+- Name: EMANet
   Metadata:
     Training Data:
     - Cityscapes
@@ -14,7 +14,7 @@ Collections:
     Code: https://xialipku.github.io/EMANet
 Models:
 - Name: emanet_r50-d8_512x1024_80k_cityscapes
-  In Collection: emanet
+  In Collection: EMANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -36,7 +36,7 @@ Models:
   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
 - Name: emanet_r101-d8_512x1024_80k_cityscapes
-  In Collection: emanet
+  In Collection: EMANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -58,7 +58,7 @@ Models:
   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
 - Name: emanet_r50-d8_769x769_80k_cityscapes
-  In Collection: emanet
+  In Collection: EMANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -80,7 +80,7 @@ Models:
   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
 - Name: emanet_r101-d8_769x769_80k_cityscapes
-  In Collection: emanet
+  In Collection: EMANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
diff --git a/configs/encnet/encnet.yml b/configs/encnet/encnet.yml
index 4876222..18fb32a 100644
--- a/configs/encnet/encnet.yml
+++ b/configs/encnet/encnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: encnet
+- Name: EncNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/zhanghang1989/PyTorch-Encoding
 Models:
 - Name: encnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: encnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   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
 - Name: encnet_r50-d8_769x769_40k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -81,7 +81,7 @@ Models:
   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
 - Name: encnet_r101-d8_769x769_40k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -103,7 +103,7 @@ Models:
   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
 - Name: encnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -117,7 +117,7 @@ Models:
   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
 - Name: encnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -131,7 +131,7 @@ Models:
   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
 - Name: encnet_r50-d8_769x769_80k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -145,7 +145,7 @@ Models:
   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
 - Name: encnet_r101-d8_769x769_80k_cityscapes
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -159,7 +159,7 @@ Models:
   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
 - Name: encnet_r50-d8_512x512_80k_ade20k
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -181,7 +181,7 @@ Models:
   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
 - Name: encnet_r101-d8_512x512_80k_ade20k
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -203,7 +203,7 @@ Models:
   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
 - Name: encnet_r50-d8_512x512_160k_ade20k
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -217,7 +217,7 @@ Models:
   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
 - Name: encnet_r101-d8_512x512_160k_ade20k
-  In Collection: encnet
+  In Collection: EncNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/erfnet/README.md b/configs/erfnet/README.md
index a0ccb61..83c74c5 100644
--- a/configs/erfnet/README.md
+++ b/configs/erfnet/README.md
@@ -42,7 +42,7 @@ Semantic segmentation is a challenging task that addresses most of the perceptio
 
 | Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
 | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN | ERFNet | 512x1024  | 160000 | 6.04 | 15.26 | 71.08 | 72.6 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056-03d333ed.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056.log.json) |
+| ERFNet | ERFNet | 512x1024  | 160000 | 6.04 | 15.26 | 71.08 | 72.6 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056-03d333ed.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056.log.json) |
 
 Note:
 
diff --git a/configs/erfnet/erfnet.yml b/configs/erfnet/erfnet.yml
index f0d8fb7..e4c34f9 100644
--- a/configs/erfnet/erfnet.yml
+++ b/configs/erfnet/erfnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: erfnet
+- Name: ERFNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/Eromera/erfnet_pytorch
 Models:
 - Name: erfnet_fcn_4x4_512x1024_160k_cityscapes
-  In Collection: erfnet
+  In Collection: ERFNet
   Metadata:
     backbone: ERFNet
     crop size: (512,1024)
diff --git a/configs/fastfcn/README.md b/configs/fastfcn/README.md
index eea2158..e04fc7b 100644
--- a/configs/fastfcn/README.md
+++ b/configs/fastfcn/README.md
@@ -38,23 +38,23 @@ year={2019}
 
 | Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
 | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 + JPU | R-50-D32 | 512x1024  | 80000 | 5.67 | 2.64 | 79.12 | 80.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json)  |
-| DeepLabV3 + JPU (4x4) | R-50-D32 | 512x1024  | 80000 | 9.79 | - | 79.52 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
-| PSPNet + JPU | R-50-D32   | 512x1024  |   80000 |  5.67       |  4.40             | 79.26 |   80.86       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py)  |  [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json)       |
-| PSPNet + JPU (4x4) | R-50-D32   | 512x1024  |   80000 |  9.94       |  -             | 78.76 |   80.03       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)         |
-| EncNet + JPU | R-50-D32  | 512x1024  |   80000 | 8.15        |  4.77             | 77.97 |79.92          | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json)    |
-| EncNet + JPU (4x4)| R-50-D32  | 512x1024  |   80000 | 15.45        | -              | 78.6 |         80.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
+| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  | 80000 | 5.67 | 2.64 | 79.12 | 80.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json)  |
+| FastFCN + DeepLabV3 (4x4) | R-50-D32 | 512x1024  | 80000 | 9.79 | - | 79.52 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
+| FastFCN + PSPNet | R-50-D32   | 512x1024  |   80000 |  5.67       |  4.40             | 79.26 |   80.86       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py)  |  [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json)       |
+| FastFCN + PSPNet (4x4) | R-50-D32   | 512x1024  |   80000 |  9.94       |  -             | 78.76 |   80.03       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)         |
+| FastFCN + EncNet | R-50-D32  | 512x1024  |   80000 | 8.15        |  4.77             | 77.97 |79.92          | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json)    |
+| FastFCN + EncNet (4x4)| R-50-D32  | 512x1024  |   80000 | 15.45        | -              | 78.6 |         80.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
 
 ### ADE20K
 
 | Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
 | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 + JPU | R-50-D32 | 512x1024  | 80000 | 8.46 | 12.06 | 41.88 | 42.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json)  |
-| DeepLabV3 + JPU | R-50-D32 | 512x1024  | 160000 | - | - | 43.58 | 44.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json) |
-| PSPNet + JPU | R-50-D32   | 512x1024  |   80000 | 8.02        |  19.21             | 41.40 |   42.12       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py)  |  [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json)       |
-| PSPNet + JPU | R-50-D32   | 512x1024  |   160000 |  -       |  -             | 42.63 |   43.71       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json)         |
-| EncNet + JPU | R-50-D32  | 512x1024  |   80000 | 9.67        |  17.23             | 40.88 | 42.36          | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json)    |
-| EncNet + JPU | R-50-D32  | 512x1024  |   160000 | -        | -              | 42.50 |         44.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json)     |
+| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  | 80000 | 8.46 | 12.06 | 41.88 | 42.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json)  |
+| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  | 160000 | - | - | 43.58 | 44.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json) |
+| FastFCN + PSPNet | R-50-D32   | 512x1024  |   80000 | 8.02        |  19.21             | 41.40 |   42.12       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py)  |  [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json)       |
+| FastFCN + PSPNet | R-50-D32   | 512x1024  |   160000 |  -       |  -             | 42.63 |   43.71       | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json)         |
+| FastFCN + EncNet | R-50-D32  | 512x1024  |   80000 | 9.67        |  17.23             | 40.88 | 42.36          | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json)    |
+| FastFCN + EncNet | R-50-D32  | 512x1024  |   160000 | -        | -              | 42.50 |         44.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json)     |
 
 Note:
 
diff --git a/configs/fastfcn/fastfcn.yml b/configs/fastfcn/fastfcn.yml
index 30aa72d..650ce5d 100644
--- a/configs/fastfcn/fastfcn.yml
+++ b/configs/fastfcn/fastfcn.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: fastfcn
+- Name: FastFCN
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/wuhuikai/FastFCN
 Models:
 - Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
 - Name: fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -52,7 +52,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
 - Name: fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -74,7 +74,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
 - Name: fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -89,7 +89,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
 - Name: fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -111,7 +111,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
 - Name: fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -126,7 +126,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
 - Name: fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -148,7 +148,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
 - Name: fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -162,7 +162,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
 - Name: fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -184,7 +184,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
 - Name: fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -198,7 +198,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
 - Name: fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
@@ -220,7 +220,7 @@ Models:
   Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
 - Name: fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k
-  In Collection: fastfcn
+  In Collection: FastFCN
   Metadata:
     backbone: R-50-D32
     crop size: (512,1024)
diff --git a/configs/fastscnn/README.md b/configs/fastscnn/README.md
index 570faff..b0023b2 100644
--- a/configs/fastscnn/README.md
+++ b/configs/fastscnn/README.md
@@ -38,4 +38,4 @@ The encoder-decoder framework is state-of-the-art for offline semantic image seg
 
 | Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
 | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Fast-SCNN | Fast-SCNN | 512x1024  | 160000 | 3.3 | 56.45 | 70.96 | 72.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py) | [model](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) &#124; [log](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.log.json) |
+| FastSCNN | FastSCNN | 512x1024  | 160000 | 3.3 | 56.45 | 70.96 | 72.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py) | [model](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) &#124; [log](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.log.json) |
diff --git a/configs/fastscnn/fastscnn.yml b/configs/fastscnn/fastscnn.yml
index d4a3e9a..cad0360 100644
--- a/configs/fastscnn/fastscnn.yml
+++ b/configs/fastscnn/fastscnn.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: fastscnn
+- Name: FastSCNN
   Metadata:
     Training Data:
     - Cityscapes
@@ -12,9 +12,9 @@ Collections:
     Version: v0.17.0
 Models:
 - Name: fast_scnn_lr0.12_8x4_160k_cityscapes
-  In Collection: fastscnn
+  In Collection: FastSCNN
   Metadata:
-    backbone: Fast-SCNN
+    backbone: FastSCNN
     crop size: (512,1024)
     lr schd: 160000
     inference time (ms/im):
diff --git a/configs/fcn/README.md b/configs/fcn/README.md
index 8863e7a..4925b00 100644
--- a/configs/fcn/README.md
+++ b/configs/fcn/README.md
@@ -59,18 +59,18 @@ Convolutional networks are powerful visual models that yield hierarchies of feat
 | FCN    | R-18b-D8   | 769x769   |   80000 | 1.7      | 6.70           | 69.66 |         72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json)         |
 | FCN    | R-50b-D8   | 769x769   |   80000 | 6.3      | 1.82           | 73.83 |         76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json)         |
 | FCN    | R-101b-D8  | 769x769   |   80000 | 10.3     | 1.15           | 77.02 |         78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json)     |
-| FCN-D6 | R-50-D16   | 512x1024  |   40000 | 3.4      | 10.22          | 77.06 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)         |
-| FCN-D6 | R-50-D16   | 512x1024  |   80000 | -        | 10.35          | 77.27 |         78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json)         |
-| FCN-D6 | R-50-D16   | 769x769   |   40000 | 3.7      | 4.17           | 76.82 |         78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json)             |
-| FCN-D6 | R-50-D16   | 769x769   |   80000 | -        | 4.15           | 77.04 |         78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json)             |
-| FCN-D6 | R-101-D16  | 512x1024  |   40000 | 4.5      | 8.04           | 77.36 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json)     |
-| FCN-D6 | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
-| FCN-D6 | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
-| FCN-D6 | R-101-D16  | 769x769   |   80000 | -        | 3.21           | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
-| FCN-D6 | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
-| FCN-D6 | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
-| FCN-D6 | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
-| FCN-D6 | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
+| FCN (D6) | R-50-D16   | 512x1024  |   40000 | 3.4      | 10.22          | 77.06 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)         |
+| FCN (D6) | R-50-D16   | 512x1024  |   80000 | -        | 10.35          | 77.27 |         78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json)         |
+| FCN (D6) | R-50-D16   | 769x769   |   40000 | 3.7      | 4.17           | 76.82 |         78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json)             |
+| FCN (D6) | R-50-D16   | 769x769   |   80000 | -        | 4.15           | 77.04 |         78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json)             |
+| FCN (D6) | R-101-D16  | 512x1024  |   40000 | 4.5      | 8.04           | 77.36 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json)     |
+| FCN (D6) | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
+| FCN (D6) | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
+| FCN (D6) | R-101-D16  | 769x769   |   80000 | -        | 3.21           | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
+| FCN (D6) | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
+| FCN (D6) | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
+| FCN (D6) | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
+| FCN (D6) | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
 
 ### ADE20K
 
@@ -107,3 +107,4 @@ Convolutional networks are powerful visual models that yield hierarchies of feat
 Note:
 
 - `FP16` means Mixed Precision (FP16) is adopted in training.
+- `FCN D6` means dilation rate of convolution operator in FCN is 6.
diff --git a/configs/fcn/fcn.yml b/configs/fcn/fcn.yml
index 276b9f5..563391c 100644
--- a/configs/fcn/fcn.yml
+++ b/configs/fcn/fcn.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: fcn
+- Name: FCN
   Metadata:
     Training Data:
     - Cityscapes
@@ -18,7 +18,7 @@ Collections:
     Code: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn
 Models:
 - Name: fcn_r50-d8_512x1024_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -40,7 +40,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x1024_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -62,7 +62,7 @@ Models:
   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
 - Name: fcn_r50-d8_769x769_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -84,7 +84,7 @@ Models:
   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
 - Name: fcn_r101-d8_769x769_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -106,7 +106,7 @@ Models:
   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
 - Name: fcn_r18-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
@@ -128,7 +128,7 @@ Models:
   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
 - Name: fcn_r50-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -142,7 +142,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -156,7 +156,7 @@ Models:
   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
 - Name: fcn_r101-d8_fp16_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -177,7 +177,7 @@ Models:
   Config: configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth
 - Name: fcn_r18-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
@@ -199,7 +199,7 @@ Models:
   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
 - Name: fcn_r50-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -213,7 +213,7 @@ Models:
   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
 - Name: fcn_r101-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -227,7 +227,7 @@ Models:
   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
 - Name: fcn_r18b-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
@@ -249,7 +249,7 @@ Models:
   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
 - Name: fcn_r50b-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
@@ -271,7 +271,7 @@ Models:
   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
 - Name: fcn_r101b-d8_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
@@ -293,7 +293,7 @@ Models:
   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
 - Name: fcn_r18b-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
@@ -315,7 +315,7 @@ Models:
   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
 - Name: fcn_r50b-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
@@ -337,7 +337,7 @@ Models:
   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
 - Name: fcn_r101b-d8_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
@@ -359,7 +359,7 @@ Models:
   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
 - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D16
     crop size: (512,1024)
@@ -381,7 +381,7 @@ Models:
   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
 - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D16
     crop size: (512,1024)
@@ -402,7 +402,7 @@ Models:
   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
 - Name: fcn_d6_r50-d16_769x769_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D16
     crop size: (769,769)
@@ -424,7 +424,7 @@ Models:
   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
 - Name: fcn_d6_r50-d16_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D16
     crop size: (769,769)
@@ -445,7 +445,7 @@ Models:
   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
 - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D16
     crop size: (512,1024)
@@ -467,7 +467,7 @@ Models:
   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
 - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D16
     crop size: (512,1024)
@@ -488,7 +488,7 @@ Models:
   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
 - Name: fcn_d6_r101-d16_769x769_40k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D16
     crop size: (769,769)
@@ -510,7 +510,7 @@ Models:
   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
 - Name: fcn_d6_r101-d16_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D16
     crop size: (769,769)
@@ -531,7 +531,7 @@ Models:
   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
 - Name: fcn_d6_r50b-d16_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50b-D16
     crop size: (512,1024)
@@ -553,7 +553,7 @@ Models:
   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
 - Name: fcn_d6_r50b-d16_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50b-D16
     crop size: (769,769)
@@ -575,7 +575,7 @@ Models:
   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
 - Name: fcn_d6_r101b-d16_512x1024_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101b-D16
     crop size: (512,1024)
@@ -597,7 +597,7 @@ Models:
   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
 - Name: fcn_d6_r101b-d16_769x769_80k_cityscapes
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101b-D16
     crop size: (769,769)
@@ -619,7 +619,7 @@ Models:
   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
 - Name: fcn_r50-d8_512x512_80k_ade20k
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -641,7 +641,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x512_80k_ade20k
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -663,7 +663,7 @@ Models:
   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
 - Name: fcn_r50-d8_512x512_160k_ade20k
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -677,7 +677,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x512_160k_ade20k
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -691,7 +691,7 @@ Models:
   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
 - Name: fcn_r50-d8_512x512_20k_voc12aug
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -713,7 +713,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x512_20k_voc12aug
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -735,7 +735,7 @@ Models:
   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
 - Name: fcn_r50-d8_512x512_40k_voc12aug
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -749,7 +749,7 @@ Models:
   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
 - Name: fcn_r101-d8_512x512_40k_voc12aug
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -763,7 +763,7 @@ Models:
   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
 - Name: fcn_r101-d8_480x480_40k_pascal_context
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -784,7 +784,7 @@ Models:
   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
 - Name: fcn_r101-d8_480x480_80k_pascal_context
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -798,7 +798,7 @@ Models:
   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
 - Name: fcn_r101-d8_480x480_40k_pascal_context_59
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -812,7 +812,7 @@ Models:
   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
 - Name: fcn_r101-d8_480x480_80k_pascal_context_59
-  In Collection: fcn
+  In Collection: FCN
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
diff --git a/configs/gcnet/gcnet.yml b/configs/gcnet/gcnet.yml
index c13849f..1d5eecf 100644
--- a/configs/gcnet/gcnet.yml
+++ b/configs/gcnet/gcnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: gcnet
+- Name: GCNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/xvjiarui/GCNet
 Models:
 - Name: gcnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: gcnet_r50-d8_769x769_40k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: gcnet_r101-d8_769x769_40k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: gcnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: gcnet_r50-d8_769x769_80k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: gcnet_r101-d8_769x769_80k_cityscapes
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: gcnet_r50-d8_512x512_80k_ade20k
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x512_80k_ade20k
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   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
 - Name: gcnet_r50-d8_512x512_160k_ade20k
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x512_160k_ade20k
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   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
 - Name: gcnet_r50-d8_512x512_20k_voc12aug
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x512_20k_voc12aug
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   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
 - Name: gcnet_r50-d8_512x512_40k_voc12aug
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: gcnet_r101-d8_512x512_40k_voc12aug
-  In Collection: gcnet
+  In Collection: GCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml
index cd989df..960a937 100644
--- a/configs/hrnet/hrnet.yml
+++ b/configs/hrnet/hrnet.yml
@@ -1,28 +1,6 @@
-Collections:
-- Name: hrnet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-    - Pascal Context
-    - Pascal Context 59
-    - LoveDA
-    - Potsdam
-    - Vaihingen
-    - iSAID
-  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:
 - Name: fcn_hr18s_512x1024_40k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -44,7 +22,7 @@ Models:
   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
 - Name: fcn_hr18_512x1024_40k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -66,7 +44,7 @@ Models:
   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
 - Name: fcn_hr48_512x1024_40k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -88,7 +66,7 @@ Models:
   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
 - Name: fcn_hr18s_512x1024_80k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -102,7 +80,7 @@ Models:
   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
 - Name: fcn_hr18_512x1024_80k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -116,7 +94,7 @@ Models:
   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
 - Name: fcn_hr48_512x1024_80k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -130,7 +108,7 @@ Models:
   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
 - Name: fcn_hr18s_512x1024_160k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -144,7 +122,7 @@ Models:
   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
 - Name: fcn_hr18_512x1024_160k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -158,7 +136,7 @@ Models:
   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
 - Name: fcn_hr48_512x1024_160k_cityscapes
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -172,7 +150,7 @@ Models:
   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
 - Name: fcn_hr18s_512x512_80k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -194,7 +172,7 @@ Models:
   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
 - Name: fcn_hr18_512x512_80k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -216,7 +194,7 @@ Models:
   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
 - Name: fcn_hr48_512x512_80k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -238,7 +216,7 @@ Models:
   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
 - Name: fcn_hr18s_512x512_160k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -252,7 +230,7 @@ Models:
   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
 - Name: fcn_hr18_512x512_160k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -266,7 +244,7 @@ Models:
   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
 - Name: fcn_hr48_512x512_160k_ade20k
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -280,7 +258,7 @@ Models:
   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
 - Name: fcn_hr18s_512x512_20k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -302,7 +280,7 @@ Models:
   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
 - Name: fcn_hr18_512x512_20k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -324,7 +302,7 @@ Models:
   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
 - Name: fcn_hr48_512x512_20k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -346,7 +324,7 @@ Models:
   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
 - Name: fcn_hr18s_512x512_40k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -360,7 +338,7 @@ Models:
   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
 - Name: fcn_hr18_512x512_40k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -374,7 +352,7 @@ Models:
   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
 - Name: fcn_hr48_512x512_40k_voc12aug
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -388,7 +366,7 @@ Models:
   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
 - Name: fcn_hr48_480x480_40k_pascal_context
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
@@ -410,7 +388,7 @@ Models:
   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
 - Name: fcn_hr48_480x480_80k_pascal_context
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
@@ -424,7 +402,7 @@ Models:
   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
 - Name: fcn_hr48_480x480_40k_pascal_context_59
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
@@ -438,7 +416,7 @@ Models:
   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
 - Name: fcn_hr48_480x480_80k_pascal_context_59
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (480,480)
@@ -452,7 +430,7 @@ Models:
   Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
 - Name: fcn_hr18s_512x512_80k_loveda
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -474,7 +452,7 @@ Models:
   Config: configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth
 - Name: fcn_hr18_512x512_80k_loveda
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -496,7 +474,7 @@ Models:
   Config: configs/hrnet/fcn_hr18_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth
 - Name: fcn_hr48_512x512_80k_loveda
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -518,7 +496,7 @@ Models:
   Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
 - Name: fcn_hr18s_512x512_80k_potsdam
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -540,7 +518,7 @@ Models:
   Config: configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
 - Name: fcn_hr18_512x512_80k_potsdam
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -562,7 +540,7 @@ Models:
   Config: configs/hrnet/fcn_hr18_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
 - Name: fcn_hr48_512x512_80k_potsdam
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -584,7 +562,7 @@ Models:
   Config: configs/hrnet/fcn_hr48_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
 - Name: fcn_hr18s_4x4_512x512_80k_vaihingen
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -606,7 +584,7 @@ Models:
   Config: configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
 - Name: fcn_hr18_4x4_512x512_80k_vaihingen
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -628,7 +606,7 @@ Models:
   Config: configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
 - Name: fcn_hr48_4x4_512x512_80k_vaihingen
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -650,7 +628,7 @@ Models:
   Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
 - Name: fcn_hr18s_4x4_896x896_80k_isaid
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (896,896)
@@ -672,7 +650,7 @@ Models:
   Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
 - Name: fcn_hr18_4x4_896x896_80k_isaid
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (896,896)
@@ -694,7 +672,7 @@ Models:
   Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
 - Name: fcn_hr48_4x4_896x896_80k_isaid
-  In Collection: hrnet
+  In Collection: FCN
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (896,896)
diff --git a/configs/icnet/icnet.yml b/configs/icnet/icnet.yml
index eb8852c..ebaf934 100644
--- a/configs/icnet/icnet.yml
+++ b/configs/icnet/icnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: icnet
+- Name: ICNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/hszhao/ICNet
 Models:
 - Name: icnet_r18-d8_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-18-D8
     crop size: (832,832)
@@ -36,7 +36,7 @@ Models:
   Config: configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
 - Name: icnet_r18-d8_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-18-D8
     crop size: (832,832)
@@ -50,7 +50,7 @@ Models:
   Config: configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
 - Name: icnet_r18-d8_in1k-pre_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-18-D8
     crop size: (832,832)
@@ -64,7 +64,7 @@ Models:
   Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
 - Name: icnet_r18-d8_in1k-pre_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-18-D8
     crop size: (832,832)
@@ -78,7 +78,7 @@ Models:
   Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
 - Name: icnet_r50-d8_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-50-D8
     crop size: (832,832)
@@ -100,7 +100,7 @@ Models:
   Config: configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
 - Name: icnet_r50-d8_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-50-D8
     crop size: (832,832)
@@ -114,7 +114,7 @@ Models:
   Config: configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
 - Name: icnet_r50-d8_in1k-pre_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-50-D8
     crop size: (832,832)
@@ -128,7 +128,7 @@ Models:
   Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
 - Name: icnet_r50-d8_in1k-pre_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-50-D8
     crop size: (832,832)
@@ -142,7 +142,7 @@ Models:
   Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
 - Name: icnet_r101-d8_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-101-D8
     crop size: (832,832)
@@ -164,7 +164,7 @@ Models:
   Config: configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
 - Name: icnet_r101-d8_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-101-D8
     crop size: (832,832)
@@ -178,7 +178,7 @@ Models:
   Config: configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
 - Name: icnet_r101-d8_in1k-pre_832x832_80k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-101-D8
     crop size: (832,832)
@@ -192,7 +192,7 @@ Models:
   Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
 - Name: icnet_r101-d8_in1k-pre_832x832_160k_cityscapes
-  In Collection: icnet
+  In Collection: ICNet
   Metadata:
     backbone: R-101-D8
     crop size: (832,832)
diff --git a/configs/isanet/isanet.yml b/configs/isanet/isanet.yml
index 212ce8c..8c65bcf 100644
--- a/configs/isanet/isanet.yml
+++ b/configs/isanet/isanet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: isanet
+- Name: ISANet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/openseg-group/openseg.pytorch
 Models:
 - Name: isanet_r50-d8_512x1024_40k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: isanet_r50-d8_512x1024_80k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: isanet_r50-d8_769x769_40k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: isanet_r50-d8_769x769_80k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x1024_40k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -126,7 +126,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x1024_80k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -148,7 +148,7 @@ Models:
   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
 - Name: isanet_r101-d8_769x769_40k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -170,7 +170,7 @@ Models:
   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
 - Name: isanet_r101-d8_769x769_80k_cityscapes
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -192,7 +192,7 @@ Models:
   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
 - Name: isanet_r50-d8_512x512_80k_ade20k
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -214,7 +214,7 @@ Models:
   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
 - Name: isanet_r50-d8_512x512_160k_ade20k
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -236,7 +236,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x512_80k_ade20k
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -258,7 +258,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x512_160k_ade20k
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -280,7 +280,7 @@ Models:
   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
 - Name: isanet_r50-d8_512x512_20k_voc12aug
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -302,7 +302,7 @@ Models:
   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
 - Name: isanet_r50-d8_512x512_40k_voc12aug
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -324,7 +324,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x512_20k_voc12aug
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -346,7 +346,7 @@ Models:
   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
 - Name: isanet_r101-d8_512x512_40k_voc12aug
-  In Collection: isanet
+  In Collection: ISANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/mobilenet_v2/mobilenet_v2.yml b/configs/mobilenet_v2/mobilenet_v2.yml
index bb0ddd6..5527ba8 100644
--- a/configs/mobilenet_v2/mobilenet_v2.yml
+++ b/configs/mobilenet_v2/mobilenet_v2.yml
@@ -1,21 +1,6 @@
-Collections:
-- Name: mobilenet_v2
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  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:
 - Name: fcn_m-v2-d8_512x1024_80k_cityscapes
-  In Collection: mobilenet_v2
+  In Collection: FCN
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
@@ -36,7 +21,7 @@ Models:
   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
 - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
-  In Collection: mobilenet_v2
+  In Collection: PSPNet
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
@@ -57,7 +42,7 @@ Models:
   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
 - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
-  In Collection: mobilenet_v2
+  In Collection: DeepLabV3
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
@@ -78,7 +63,7 @@ Models:
   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
 - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
-  In Collection: mobilenet_v2
+  In Collection: DeepLabV3+
   Metadata:
     backbone: M-V2-D8
     crop size: (512,1024)
@@ -99,7 +84,7 @@ Models:
   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
 - Name: fcn_m-v2-d8_512x512_160k_ade20k
-  In Collection: mobilenet_v2
+  In Collection: FCN
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
@@ -120,7 +105,7 @@ Models:
   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
 - Name: pspnet_m-v2-d8_512x512_160k_ade20k
-  In Collection: mobilenet_v2
+  In Collection: PSPNet
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
@@ -141,7 +126,7 @@ Models:
   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
 - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
-  In Collection: mobilenet_v2
+  In Collection: DeepLabV3
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
@@ -162,7 +147,7 @@ Models:
   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
 - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
-  In Collection: mobilenet_v2
+  In Collection: DeepLabV3+
   Metadata:
     backbone: M-V2-D8
     crop size: (512,512)
diff --git a/configs/mobilenet_v3/mobilenet_v3.yml b/configs/mobilenet_v3/mobilenet_v3.yml
index 3878dba..81a1796 100644
--- a/configs/mobilenet_v3/mobilenet_v3.yml
+++ b/configs/mobilenet_v3/mobilenet_v3.yml
@@ -1,20 +1,6 @@
-Collections:
-- Name: mobilenet_v3
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/1905.02244
-    Title: Searching for MobileNetV3
-  README: configs/mobilenet_v3/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
 Models:
 - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
-  In Collection: mobilenet_v3
+  In Collection: LRASPP
   Metadata:
     backbone: M-V3-D8
     crop size: (512,1024)
@@ -36,7 +22,7 @@ Models:
   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
 - Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
-  In Collection: mobilenet_v3
+  In Collection: LRASPP
   Metadata:
     backbone: M-V3-D8 (scratch)
     crop size: (512,1024)
@@ -58,7 +44,7 @@ Models:
   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
 - Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
-  In Collection: mobilenet_v3
+  In Collection: LRASPP
   Metadata:
     backbone: M-V3s-D8
     crop size: (512,1024)
@@ -80,7 +66,7 @@ Models:
   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
 - Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
-  In Collection: mobilenet_v3
+  In Collection: LRASPP
   Metadata:
     backbone: M-V3s-D8 (scratch)
     crop size: (512,1024)
diff --git a/configs/nonlocal_net/README.md b/configs/nonlocal_net/README.md
index 787d87b..e1bd9d4 100644
--- a/configs/nonlocal_net/README.md
+++ b/configs/nonlocal_net/README.md
@@ -39,29 +39,29 @@ Both convolutional and recurrent operations are building blocks that process one
 
 | Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                                         |
 | -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.72           | 78.24 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json)     |
-| NonLocal | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.95           | 78.66 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) |
-| NonLocal | R-50-D8  | 769x769   |   40000 | 8.9      | 1.52           | 78.33 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json)         |
-| NonLocal | R-101-D8 | 769x769   |   40000 | 12.8     | 1.05           | 78.57 | 80.29         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json)     |
-| NonLocal | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.01 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json)     |
-| NonLocal | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.93 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) |
-| NonLocal | R-50-D8  | 769x769   |   80000 | -        | -              | 79.05 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json)         |
-| NonLocal | R-101-D8 | 769x769   |   80000 | -        | -              | 79.40 | 80.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json)     |
+| NonLocalNet | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.72           | 78.24 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json)     |
+| NonLocalNet | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.95           | 78.66 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) |
+| NonLocalNet | R-50-D8  | 769x769   |   40000 | 8.9      | 1.52           | 78.33 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json)         |
+| NonLocalNet | R-101-D8 | 769x769   |   40000 | 12.8     | 1.05           | 78.57 | 80.29         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json)     |
+| NonLocalNet | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.01 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json)     |
+| NonLocalNet | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.93 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) |
+| NonLocalNet | R-50-D8  | 769x769   |   80000 | -        | -              | 79.05 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json)         |
+| NonLocalNet | R-101-D8 | 769x769   |   80000 | -        | -              | 79.40 | 80.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json)     |
 
 ### ADE20K
 
 | Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                         |
 | -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x512   |   80000 | 9.1      | 21.37          | 40.75 |         42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json)         |
-| NonLocal | R-101-D8 | 512x512   |   80000 | 12.6     | 13.97          | 42.90 |         44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json)     |
-| NonLocal | R-50-D8  | 512x512   |  160000 | -        | -              | 42.03 |         43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |  160000 | -        | -              | 44.63 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502.log.json) |
+| NonLocalNet | R-50-D8  | 512x512   |   80000 | 9.1      | 21.37          | 40.75 |         42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py)   | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json)         |
+| NonLocalNet | R-101-D8 | 512x512   |   80000 | 12.6     | 13.97          | 42.90 |         44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json)     |
+| NonLocalNet | R-50-D8  | 512x512   |  160000 | -        | -              | 42.03 |         43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |  160000 | -        | -              | 44.63 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
 | Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                             |
 | -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocal | R-50-D8  | 512x512   |   20000 | 6.4      | 21.21          | 76.20 |         77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |   20000 | 9.8      | 14.01          | 78.15 |         78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) |
-| NonLocal | R-50-D8  | 512x512   |   40000 | -        | -              | 76.65 |         77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json)     |
-| NonLocal | R-101-D8 | 512x512   |   40000 | -        | -              | 78.27 |         79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) |
+| NonLocalNet | R-50-D8  | 512x512   |   20000 | 6.4      | 21.21          | 76.20 |         77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |   20000 | 9.8      | 14.01          | 78.15 |         78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) |
+| NonLocalNet | R-50-D8  | 512x512   |   40000 | -        | -              | 76.65 |         77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py)  | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |   40000 | -        | -              | 78.27 |         79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) |
diff --git a/configs/nonlocal_net/nonlocal_net.yml b/configs/nonlocal_net/nonlocal_net.yml
index 8122873..bab38ce 100644
--- a/configs/nonlocal_net/nonlocal_net.yml
+++ b/configs/nonlocal_net/nonlocal_net.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: nonlocal_net
+- Name: NonLocalNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/facebookresearch/video-nonlocal-net
 Models:
 - Name: nonlocal_r50-d8_512x1024_40k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x1024_40k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -58,7 +58,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_769x769_40k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -80,7 +80,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_769x769_40k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -102,7 +102,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_512x1024_80k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -115,7 +115,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x1024_80k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -128,7 +128,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_769x769_80k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -142,7 +142,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_769x769_80k_cityscapes
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -156,7 +156,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_512x512_80k_ade20k
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -178,7 +178,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x512_80k_ade20k
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -200,7 +200,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_512x512_160k_ade20k
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -214,7 +214,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x512_160k_ade20k
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -228,7 +228,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_512x512_20k_voc12aug
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -250,7 +250,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x512_20k_voc12aug
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -272,7 +272,7 @@ Models:
   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
 - Name: nonlocal_r50-d8_512x512_40k_voc12aug
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -286,7 +286,7 @@ Models:
   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
 - Name: nonlocal_r101-d8_512x512_40k_voc12aug
-  In Collection: nonlocal_net
+  In Collection: NonLocalNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/ocrnet/ocrnet.yml b/configs/ocrnet/ocrnet.yml
index 90f1793..d599f0a 100644
--- a/configs/ocrnet/ocrnet.yml
+++ b/configs/ocrnet/ocrnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: ocrnet
+- Name: OCRNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/openseg-group/OCNet.pytorch
 Models:
 - Name: ocrnet_hr18s_512x1024_40k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x1024_40k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x1024_40k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x1024_80k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -96,7 +96,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x1024_80k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -110,7 +110,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x1024_80k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -124,7 +124,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x1024_160k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,1024)
@@ -138,7 +138,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x1024_160k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,1024)
@@ -152,7 +152,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x1024_160k_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,1024)
@@ -166,7 +166,7 @@ Models:
   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
 - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -179,7 +179,7 @@ Models:
   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
 - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -200,7 +200,7 @@ Models:
   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
 - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -221,7 +221,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x512_80k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -243,7 +243,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x512_80k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -265,7 +265,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x512_80k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -287,7 +287,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x512_160k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -301,7 +301,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x512_160k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -315,7 +315,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x512_160k_ade20k
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -329,7 +329,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x512_20k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -351,7 +351,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x512_20k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -373,7 +373,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x512_20k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
@@ -395,7 +395,7 @@ Models:
   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
 - Name: ocrnet_hr18s_512x512_40k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18-Small
     crop size: (512,512)
@@ -409,7 +409,7 @@ Models:
   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
 - Name: ocrnet_hr18_512x512_40k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W18
     crop size: (512,512)
@@ -423,7 +423,7 @@ Models:
   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
 - Name: ocrnet_hr48_512x512_40k_voc12aug
-  In Collection: ocrnet
+  In Collection: OCRNet
   Metadata:
     backbone: HRNetV2p-W48
     crop size: (512,512)
diff --git a/configs/point_rend/point_rend.yml b/configs/point_rend/point_rend.yml
index d3de2d2..3abe81d 100644
--- a/configs/point_rend/point_rend.yml
+++ b/configs/point_rend/point_rend.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: point_rend
+- Name: PointRend
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend
 Models:
 - Name: pointrend_r50_512x1024_80k_cityscapes
-  In Collection: point_rend
+  In Collection: PointRend
   Metadata:
     backbone: R-50
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: pointrend_r101_512x1024_80k_cityscapes
-  In Collection: point_rend
+  In Collection: PointRend
   Metadata:
     backbone: R-101
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   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
 - Name: pointrend_r50_512x512_160k_ade20k
-  In Collection: point_rend
+  In Collection: PointRend
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -81,7 +81,7 @@ Models:
   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
 - Name: pointrend_r101_512x512_160k_ade20k
-  In Collection: point_rend
+  In Collection: PointRend
   Metadata:
     backbone: R-101
     crop size: (512,512)
diff --git a/configs/psanet/psanet.yml b/configs/psanet/psanet.yml
index b64bb04..353c890 100644
--- a/configs/psanet/psanet.yml
+++ b/configs/psanet/psanet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: psanet
+- Name: PSANet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/hszhao/PSANet
 Models:
 - Name: psanet_r50-d8_512x1024_40k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x1024_40k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: psanet_r50-d8_769x769_40k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: psanet_r101-d8_769x769_40k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: psanet_r50-d8_512x1024_80k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x1024_80k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: psanet_r50-d8_769x769_80k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: psanet_r101-d8_769x769_80k_cityscapes
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: psanet_r50-d8_512x512_80k_ade20k
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x512_80k_ade20k
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   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
 - Name: psanet_r50-d8_512x512_160k_ade20k
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x512_160k_ade20k
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   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
 - Name: psanet_r50-d8_512x512_20k_voc12aug
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x512_20k_voc12aug
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   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
 - Name: psanet_r50-d8_512x512_40k_voc12aug
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: psanet_r101-d8_512x512_40k_voc12aug
-  In Collection: psanet
+  In Collection: PSANet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml
index 087367b..25d16cd 100644
--- a/configs/pspnet/pspnet.yml
+++ b/configs/pspnet/pspnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: pspnet
+- Name: PSPNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -25,7 +25,7 @@ Collections:
     Code: https://github.com/hszhao/PSPNet
 Models:
 - Name: pspnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -47,7 +47,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -69,7 +69,7 @@ Models:
   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
 - Name: pspnet_r50-d8_769x769_40k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -91,7 +91,7 @@ Models:
   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
 - Name: pspnet_r101-d8_769x769_40k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -113,7 +113,7 @@ Models:
   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
 - Name: pspnet_r18-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (512,1024)
@@ -135,7 +135,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -149,7 +149,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -163,7 +163,7 @@ Models:
   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
 - Name: pspnet_r101-d8_fp16_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -184,7 +184,7 @@ Models:
   Config: configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth
 - Name: pspnet_r18-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (769,769)
@@ -206,7 +206,7 @@ Models:
   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
 - Name: pspnet_r50-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -220,7 +220,7 @@ Models:
   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
 - Name: pspnet_r101-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -234,7 +234,7 @@ Models:
   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
 - Name: pspnet_r18b-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18b-D8
     crop size: (512,1024)
@@ -256,7 +256,7 @@ Models:
   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
 - Name: pspnet_r50b-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50b-D8
     crop size: (512,1024)
@@ -278,7 +278,7 @@ Models:
   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
 - Name: pspnet_r101b-d8_512x1024_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101b-D8
     crop size: (512,1024)
@@ -300,7 +300,7 @@ Models:
   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
 - Name: pspnet_r18b-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18b-D8
     crop size: (769,769)
@@ -322,7 +322,7 @@ Models:
   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
 - Name: pspnet_r50b-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50b-D8
     crop size: (769,769)
@@ -344,7 +344,7 @@ Models:
   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
 - Name: pspnet_r101b-d8_769x769_80k_cityscapes
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101b-D8
     crop size: (769,769)
@@ -366,7 +366,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_80k_ade20k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -388,7 +388,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_80k_ade20k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -410,7 +410,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_160k_ade20k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -424,7 +424,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_160k_ade20k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -438,7 +438,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_20k_voc12aug
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -460,7 +460,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_20k_voc12aug
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -482,7 +482,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_40k_voc12aug
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -496,7 +496,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_40k_voc12aug
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -510,7 +510,7 @@ Models:
   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
 - Name: pspnet_r101-d8_480x480_40k_pascal_context
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -532,7 +532,7 @@ Models:
   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
 - Name: pspnet_r101-d8_480x480_80k_pascal_context
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -546,7 +546,7 @@ Models:
   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
 - Name: pspnet_r101-d8_480x480_40k_pascal_context_59
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -560,7 +560,7 @@ Models:
   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
 - Name: pspnet_r101-d8_480x480_80k_pascal_context_59
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (480,480)
@@ -574,7 +574,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -596,7 +596,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -618,7 +618,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -632,7 +632,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -646,7 +646,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -668,7 +668,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -690,7 +690,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -704,7 +704,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -718,7 +718,7 @@ Models:
   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
 - Name: pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -732,7 +732,7 @@ Models:
   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
 - Name: pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -746,7 +746,7 @@ Models:
   Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
 - Name: pspnet_r18-d8_512x512_80k_loveda
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -768,7 +768,7 @@ Models:
   Config: configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
 - Name: pspnet_r50-d8_512x512_80k_loveda
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -790,7 +790,7 @@ Models:
   Config: configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
 - Name: pspnet_r101-d8_512x512_80k_loveda
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -812,7 +812,7 @@ Models:
   Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
 - Name: pspnet_r18-d8_4x4_512x512_80k_potsdam
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -834,7 +834,7 @@ Models:
   Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
 - Name: pspnet_r50-d8_4x4_512x512_80k_potsdam
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -856,7 +856,7 @@ Models:
   Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
 - Name: pspnet_r101-d8_4x4_512x512_80k_potsdam
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -878,7 +878,7 @@ Models:
   Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
 - Name: pspnet_r18-d8_4x4_512x512_80k_vaihingen
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (512,512)
@@ -900,7 +900,7 @@ Models:
   Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth
 - Name: pspnet_r50-d8_4x4_512x512_80k_vaihingen
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -922,7 +922,7 @@ Models:
   Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth
 - Name: pspnet_r101-d8_4x4_512x512_80k_vaihingen
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -944,7 +944,7 @@ Models:
   Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
 - Name: pspnet_r18-d8_4x4_896x896_80k_isaid
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-18-D8
     crop size: (896,896)
@@ -966,7 +966,7 @@ Models:
   Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
 - Name: pspnet_r50-d8_4x4_896x896_80k_isaid
-  In Collection: pspnet
+  In Collection: PSPNet
   Metadata:
     backbone: R-50-D8
     crop size: (896,896)
diff --git a/configs/resnest/resnest.yml b/configs/resnest/resnest.yml
index cd92409..b2ca259 100644
--- a/configs/resnest/resnest.yml
+++ b/configs/resnest/resnest.yml
@@ -1,21 +1,6 @@
-Collections:
-- Name: resnest
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  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:
 - Name: fcn_s101-d8_512x1024_80k_cityscapes
-  In Collection: resnest
+  In Collection: FCN
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
@@ -37,7 +22,7 @@ Models:
   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
 - Name: pspnet_s101-d8_512x1024_80k_cityscapes
-  In Collection: resnest
+  In Collection: PSPNet
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
@@ -59,7 +44,7 @@ Models:
   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
 - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
-  In Collection: resnest
+  In Collection: DeepLabV3
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
@@ -81,7 +66,7 @@ Models:
   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
 - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
-  In Collection: resnest
+  In Collection: DeepLabV3+
   Metadata:
     backbone: S-101-D8
     crop size: (512,1024)
@@ -103,7 +88,7 @@ Models:
   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
 - Name: fcn_s101-d8_512x512_160k_ade20k
-  In Collection: resnest
+  In Collection: FCN
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
@@ -125,7 +110,7 @@ Models:
   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
 - Name: pspnet_s101-d8_512x512_160k_ade20k
-  In Collection: resnest
+  In Collection: PSPNet
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
@@ -147,7 +132,7 @@ Models:
   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
 - Name: deeplabv3_s101-d8_512x512_160k_ade20k
-  In Collection: resnest
+  In Collection: DeepLabV3
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
@@ -169,7 +154,7 @@ Models:
   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
 - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
-  In Collection: resnest
+  In Collection: DeepLabV3+
   Metadata:
     backbone: S-101-D8
     crop size: (512,512)
diff --git a/configs/segformer/segformer.yml b/configs/segformer/segformer.yml
index c4efc58..d28cb16 100644
--- a/configs/segformer/segformer.yml
+++ b/configs/segformer/segformer.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: segformer
+- Name: Segformer
   Metadata:
     Training Data:
     - ADE20K
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/NVlabs/SegFormer
 Models:
 - Name: segformer_mit-b0_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B0
     crop size: (512,512)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: segformer_mit-b1_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B1
     crop size: (512,512)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: segformer_mit-b2_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B2
     crop size: (512,512)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: segformer_mit-b3_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B3
     crop size: (512,512)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: segformer_mit-b4_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B4
     crop size: (512,512)
@@ -126,7 +126,7 @@ Models:
   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
 - Name: segformer_mit-b5_512x512_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B5
     crop size: (512,512)
@@ -148,7 +148,7 @@ Models:
   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
 - Name: segformer_mit-b5_640x640_160k_ade20k
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B5
     crop size: (640,640)
@@ -170,7 +170,7 @@ Models:
   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
 - Name: segformer_mit-b0_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B0
     crop size: (1024,1024)
@@ -192,7 +192,7 @@ Models:
   Config: configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth
 - Name: segformer_mit-b1_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B1
     crop size: (1024,1024)
@@ -214,7 +214,7 @@ Models:
   Config: configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth
 - Name: segformer_mit-b2_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B2
     crop size: (1024,1024)
@@ -236,7 +236,7 @@ Models:
   Config: configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth
 - Name: segformer_mit-b3_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B3
     crop size: (1024,1024)
@@ -258,7 +258,7 @@ Models:
   Config: configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth
 - Name: segformer_mit-b4_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B4
     crop size: (1024,1024)
@@ -280,7 +280,7 @@ Models:
   Config: configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth
 - Name: segformer_mit-b5_8x1_1024x1024_160k_cityscapes
-  In Collection: segformer
+  In Collection: Segformer
   Metadata:
     backbone: MIT-B5
     crop size: (1024,1024)
diff --git a/configs/segmenter/README.md b/configs/segmenter/README.md
index b073e88..29ba260 100644
--- a/configs/segmenter/README.md
+++ b/configs/segmenter/README.md
@@ -66,8 +66,8 @@ In our default setting, pretrained models and their corresponding [ViT-AugReg](h
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config | download |
 | ------ | -------- | --------- | ---------- | ------- | -------- | --- | --- | -------------- | ----- |
-| Segmenter-Mask | ViT-T_16 | 512x512 | 160000   | 1.21        | 27.98              | 39.99 | 40.83            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json) |
-| Segmenter-Linear | ViT-S_16 | 512x512 | 160000   | 1.78        | 28.07              | 45.75 | 46.82            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713.log.json) |
-| Segmenter-Mask | ViT-S_16 | 512x512 | 160000   | 2.03        | 24.80              | 46.19 | 47.85            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json) |
-| Segmenter-Mask | ViT-B_16 |512x512 |  160000   | 4.20        | 13.20              | 49.60 | 51.07            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)     |
-| Segmenter-Mask | ViT-L_16 |640x640 |  160000   | 16.56        | 2.62              | 52.16 | 53.65            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750.log.json)     |
+| Segmenter Mask | ViT-T_16 | 512x512 | 160000   | 1.21        | 27.98              | 39.99 | 40.83            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json) |
+| Segmenter Linear | ViT-S_16 | 512x512 | 160000   | 1.78        | 28.07              | 45.75 | 46.82            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713.log.json) |
+| Segmenter Mask | ViT-S_16 | 512x512 | 160000   | 2.03        | 24.80              | 46.19 | 47.85            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json) |
+| Segmenter Mask | ViT-B_16 |512x512 |  160000   | 4.20        | 13.20              | 49.60 | 51.07            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)     |
+| Segmenter Mask | ViT-L_16 |640x640 |  160000   | 16.56        | 2.62              | 52.16 | 53.65            | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750.log.json)     |
diff --git a/configs/segmenter/segmenter.yml b/configs/segmenter/segmenter.yml
index 67cec89..dc6e68d 100644
--- a/configs/segmenter/segmenter.yml
+++ b/configs/segmenter/segmenter.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: segmenter
+- Name: Segmenter
   Metadata:
     Training Data:
     - ADE20K
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/rstrudel/segmenter
 Models:
 - Name: segmenter_vit-t_mask_8x1_512x512_160k_ade20k
-  In Collection: segmenter
+  In Collection: Segmenter
   Metadata:
     backbone: ViT-T_16
     crop size: (512,512)
@@ -36,7 +36,7 @@ Models:
   Config: configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth
 - Name: segmenter_vit-s_linear_8x1_512x512_160k_ade20k
-  In Collection: segmenter
+  In Collection: Segmenter
   Metadata:
     backbone: ViT-S_16
     crop size: (512,512)
@@ -58,7 +58,7 @@ Models:
   Config: configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth
 - Name: segmenter_vit-s_mask_8x1_512x512_160k_ade20k
-  In Collection: segmenter
+  In Collection: Segmenter
   Metadata:
     backbone: ViT-S_16
     crop size: (512,512)
@@ -80,7 +80,7 @@ Models:
   Config: configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth
 - Name: segmenter_vit-b_mask_8x1_512x512_160k_ade20k
-  In Collection: segmenter
+  In Collection: Segmenter
   Metadata:
     backbone: ViT-B_16
     crop size: (512,512)
@@ -102,7 +102,7 @@ Models:
   Config: configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth
 - Name: segmenter_vit-l_mask_8x1_512x512_160k_ade20k
-  In Collection: segmenter
+  In Collection: Segmenter
   Metadata:
     backbone: ViT-L_16
     crop size: (640,640)
diff --git a/configs/sem_fpn/sem_fpn.yml b/configs/sem_fpn/sem_fpn.yml
index b989503..d7ebdfe 100644
--- a/configs/sem_fpn/sem_fpn.yml
+++ b/configs/sem_fpn/sem_fpn.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: sem_fpn
+- Name: FPN
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/facebookresearch/detectron2
 Models:
 - Name: fpn_r50_512x1024_80k_cityscapes
-  In Collection: sem_fpn
+  In Collection: FPN
   Metadata:
     backbone: R-50
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   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
 - Name: fpn_r101_512x1024_80k_cityscapes
-  In Collection: sem_fpn
+  In Collection: FPN
   Metadata:
     backbone: R-101
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   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
 - Name: fpn_r50_512x512_160k_ade20k
-  In Collection: sem_fpn
+  In Collection: FPN
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -81,7 +81,7 @@ Models:
   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
 - Name: fpn_r101_512x512_160k_ade20k
-  In Collection: sem_fpn
+  In Collection: FPN
   Metadata:
     backbone: R-101
     crop size: (512,512)
diff --git a/configs/setr/README.md b/configs/setr/README.md
index 3a28635..e42be7e 100644
--- a/configs/setr/README.md
+++ b/configs/setr/README.md
@@ -59,15 +59,15 @@ This script convert the model from `PRETRAIN_PATH` and store the converted model
 
 | Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                     |
 | ------ | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| SETR-Naive | ViT-L | 512x512  | 16          | 160000   | 18.40        | 4.72              | 48.28 |             49.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_naive_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json)     |
-| SETR-PUP | ViT-L | 512x512  | 16          | 160000   | 19.54        | 4.50              | 48.24 |             49.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_pup_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json)     |
-| SETR-MLA | ViT-L | 512x512  | 8           | 160000   | 10.96        | -              | 47.34 |             49.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_mla_512x512_160k_b8_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json)     |
-| SETR-MLA | ViT-L | 512x512  | 16          | 160000   | 17.30        | 5.25              | 47.54 |             49.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_mla_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json)     |
+| SETR Naive | ViT-L | 512x512  | 16          | 160000   | 18.40        | 4.72              | 48.28 |             49.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_naive_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json)     |
+| SETR PUP | ViT-L | 512x512  | 16          | 160000   | 19.54        | 4.50              | 48.24 |             49.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_pup_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json)     |
+| SETR MLA | ViT-L | 512x512  | 8           | 160000   | 10.96        | -              | 47.34 |             49.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_mla_512x512_160k_b8_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json)     |
+| SETR MLA | ViT-L | 512x512  | 16          | 160000   | 17.30        | 5.25              | 47.54 |             49.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_mla_512x512_160k_b16_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json)     |
 
 ### Cityscapes
 
 | Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                     |
 | ------ | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| SETR-Naive | ViT-L | 768x768 | 8         | 80000 | 24.06 | 0.39 | 78.10 | 80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json)     |
-| SETR-PUP | ViT-L | 768x768 | 8         | 80000   | 27.96 | 0.37 | 79.21 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json)     |
-| SETR-MLA | ViT-L | 768x768 | 8           | 80000 | 24.10 | 0.41 | 77.00 | 79.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json)     |
+| SETR Naive | ViT-L | 768x768 | 8         | 80000 | 24.06 | 0.39 | 78.10 | 80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json)     |
+| SETR PUP | ViT-L | 768x768 | 8         | 80000   | 27.96 | 0.37 | 79.21 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json)     |
+| SETR MLA | ViT-L | 768x768 | 8           | 80000 | 24.10 | 0.41 | 77.00 | 79.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json)     |
diff --git a/configs/setr/setr.yml b/configs/setr/setr.yml
index c438d7e..27f58e4 100644
--- a/configs/setr/setr.yml
+++ b/configs/setr/setr.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: setr
+- Name: SETR
   Metadata:
     Training Data:
     - ADE20K
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/fudan-zvg/SETR
 Models:
 - Name: setr_naive_512x512_160k_b16_ade20k
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: setr_pup_512x512_160k_b16_ade20k
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: setr_mla_512x512_160k_b8_ade20k
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
@@ -75,7 +75,7 @@ Models:
   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
 - Name: setr_mla_512x512_160k_b16_ade20k
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (512,512)
@@ -97,7 +97,7 @@ Models:
   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
 - Name: setr_vit-large_naive_8x1_768x768_80k_cityscapes
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (768,768)
@@ -119,7 +119,7 @@ Models:
   Config: configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth
 - Name: setr_vit-large_pup_8x1_768x768_80k_cityscapes
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (768,768)
@@ -141,7 +141,7 @@ Models:
   Config: configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth
 - Name: setr_vit-large_mla_8x1_768x768_80k_cityscapes
-  In Collection: setr
+  In Collection: SETR
   Metadata:
     backbone: ViT-L
     crop size: (768,768)
diff --git a/configs/stdc/README.md b/configs/stdc/README.md
index 4fece6f..d80db1e 100644
--- a/configs/stdc/README.md
+++ b/configs/stdc/README.md
@@ -59,10 +59,10 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 | Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
 | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| STDC1 (No Pretrain) | STDC1 | 512x1024  | 80000 | 7.15 | 23.06 | 71.52 | 73.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc1_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20211125_211245-2c8ba4c5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20211125_211245.log.json) |
-| STDC1| STDC1 | 512x1024  | 80000 | - | - | 75.10 | 77.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20211125_213942-880bb7d0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20211125_213942.log.json) |
-| STDC2 (No Pretrain) | STDC2 | 512x1024  | 80000 | 8.27 | 23.71 | 73.20 | 75.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc2_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20211125_222450-82333ae0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20211125_222450.log.json) |
-| STDC2 | STDC2 | 512x1024  | 80000 | - | - | 77.17 | 79.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20211125_220437-d2c469f8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20211125_220437.log.json) |
+| STDC 1 (No Pretrain) | STDC1 | 512x1024  | 80000 | 7.15 | 23.06 | 71.52 | 73.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc1_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20211125_211245-2c8ba4c5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20211125_211245.log.json) |
+| STDC 1| STDC1 | 512x1024  | 80000 | - | - | 75.10 | 77.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20211125_213942-880bb7d0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20211125_213942.log.json) |
+| STDC 2 (No Pretrain) | STDC2 | 512x1024  | 80000 | 8.27 | 23.71 | 73.20 | 75.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc2_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20211125_222450-82333ae0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20211125_222450.log.json) |
+| STDC 2 | STDC2 | 512x1024  | 80000 | - | - | 77.17 | 79.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20211125_220437-d2c469f8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20211125_220437.log.json) |
 
 Note:
 
diff --git a/configs/stdc/stdc.yml b/configs/stdc/stdc.yml
index 39ad081..b609728 100644
--- a/configs/stdc/stdc.yml
+++ b/configs/stdc/stdc.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: stdc
+- Name: STDC
   Metadata:
     Training Data:
     - Cityscapes
@@ -14,7 +14,7 @@ Collections:
     Code: https://github.com/MichaelFan01/STDC-Seg
 Models:
 - Name: stdc1_512x1024_80k_cityscapes
-  In Collection: stdc
+  In Collection: STDC
   Metadata:
     backbone: STDC1
     crop size: (512,1024)
@@ -36,7 +36,7 @@ Models:
   Config: configs/stdc/stdc1_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20211125_211245-2c8ba4c5.pth
 - Name: stdc1_in1k-pre_512x1024_80k_cityscapes
-  In Collection: stdc
+  In Collection: STDC
   Metadata:
     backbone: STDC1
     crop size: (512,1024)
@@ -50,7 +50,7 @@ Models:
   Config: configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20211125_213942-880bb7d0.pth
 - Name: stdc2_512x1024_80k_cityscapes
-  In Collection: stdc
+  In Collection: STDC
   Metadata:
     backbone: STDC2
     crop size: (512,1024)
@@ -72,7 +72,7 @@ Models:
   Config: configs/stdc/stdc2_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20211125_222450-82333ae0.pth
 - Name: stdc2_in1k-pre_512x1024_80k_cityscapes
-  In Collection: stdc
+  In Collection: STDC
   Metadata:
     backbone: STDC2
     crop size: (512,1024)
diff --git a/configs/swin/swin.yml b/configs/swin/swin.yml
index cf42608..cf7c465 100644
--- a/configs/swin/swin.yml
+++ b/configs/swin/swin.yml
@@ -1,20 +1,6 @@
-Collections:
-- Name: swin
-  Metadata:
-    Training Data:
-    - ADE20K
-  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:
 - Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-T
     crop size: (512,512)
@@ -36,7 +22,7 @@ Models:
   Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
 - Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-S
     crop size: (512,512)
@@ -58,7 +44,7 @@ Models:
   Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
 - Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -80,7 +66,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
 - Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -94,7 +80,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
 - Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
@@ -116,7 +102,7 @@ Models:
   Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
 - Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
-  In Collection: swin
+  In Collection: UperNet
   Metadata:
     backbone: Swin-B
     crop size: (512,512)
diff --git a/configs/twins/twins.yml b/configs/twins/twins.yml
index 98c8401..6b5f5c1 100644
--- a/configs/twins/twins.yml
+++ b/configs/twins/twins.yml
@@ -1,20 +1,6 @@
-Collections:
-- Name: twins
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/pdf/2104.13840.pdf
-    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
-  README: configs/twins/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
-    Version: v0.20.0
-  Converted From:
-    Code: https://github.com/Meituan-AutoML/Twins
 Models:
 - Name: twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: PCPVT-S
     crop size: (512,512)
@@ -36,7 +22,7 @@ Models:
   Config: configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth
 - Name: twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: PCPVT-S
     crop size: (512,512)
@@ -58,7 +44,7 @@ Models:
   Config: configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth
 - Name: twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: PCPVT-B
     crop size: (512,512)
@@ -80,7 +66,7 @@ Models:
   Config: configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth
 - Name: twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: PCPVT-B
     crop size: (512,512)
@@ -102,7 +88,7 @@ Models:
   Config: configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth
 - Name: twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: PCPVT-L
     crop size: (512,512)
@@ -124,7 +110,7 @@ Models:
   Config: configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth
 - Name: twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: PCPVT-L
     crop size: (512,512)
@@ -146,7 +132,7 @@ Models:
   Config: configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth
 - Name: twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: SVT-S
     crop size: (512,512)
@@ -168,7 +154,7 @@ Models:
   Config: configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth
 - Name: twins_svt-s_uperhead_8x2_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: SVT-S
     crop size: (512,512)
@@ -190,7 +176,7 @@ Models:
   Config: configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth
 - Name: twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: SVT-B
     crop size: (512,512)
@@ -212,7 +198,7 @@ Models:
   Config: configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth
 - Name: twins_svt-b_uperhead_8x2_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: SVT-B
     crop size: (512,512)
@@ -234,7 +220,7 @@ Models:
   Config: configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth
 - Name: twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k
-  In Collection: twins
+  In Collection: FPN
   Metadata:
     backbone: SVT-L
     crop size: (512,512)
@@ -256,7 +242,7 @@ Models:
   Config: configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth
 - Name: twins_svt-l_uperhead_8x2_512x512_160k_ade20k
-  In Collection: twins
+  In Collection: UPerNet
   Metadata:
     backbone: SVT-L
     crop size: (512,512)
diff --git a/configs/unet/README.md b/configs/unet/README.md
index 96bc98d..9b055ee 100644
--- a/configs/unet/README.md
+++ b/configs/unet/README.md
@@ -40,52 +40,52 @@ There is large consent that successful training of deep networks requires many t
 
 | Method | Backbone | Loss | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                         |
 | ------ | --------- | --- |--------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN | UNet-S5-D16  | Cross Entropy | 512x1024  |   160000 | 17.91      | 3.05           | 69.10 |         71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json)         |
+| UNet + FCN | UNet-S5-D16  | Cross Entropy | 512x1024  |   160000 | 17.91      | 3.05           | 69.10 |         71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json)         |
 
 
 ### DRIVE
 
 |  Method   | Backbone     | Loss        | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  mDice | Dice | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                             |
 | ----------- | --------- | -------------------- |---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-|  FCN |  UNet-S5-D16 | Cross Entropy     | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                         |
-|  FCN |  UNet-S5-D16 | Cross Entropy + Dice     | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json)         |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)             |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.585    |              - | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json)  |
-|  DeepLabV3 | UNet-S5-D16 | Cross Entropy | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | 88.38 |78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
-|  DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
+|  UNet + FCN |  UNet-S5-D16 | Cross Entropy     | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                         |
+|  UNet + FCN |  UNet-S5-D16 | Cross Entropy + Dice     | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json)         |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)             |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.585    |              - | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json)  |
+|  UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | 88.38 |78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
+|  UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
 
 ### STARE
 
 |   Method   | Backbone | Loss     | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  mDice | Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------| --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-|  FCN |   UNet-S5-D16 | Cross Entropy   | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                         |
-|  FCN |   UNet-S5-D16 | Cross Entropy + Dice   | 605x700    | 128x128   |  85x85 | 40000   | 0.986   |              - | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json)                         |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)             |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |   85x85 | 40000   | 1.028    |              - | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json)             |
-|  DeepLabV3 | UNet-S5-D16 | Cross Entropy | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
-|  DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.010    |              - | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
+|  UNet + FCN |   UNet-S5-D16 | Cross Entropy   | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                         |
+|  UNet + FCN |   UNet-S5-D16 | Cross Entropy + Dice   | 605x700    | 128x128   |  85x85 | 40000   | 0.986   |              - | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json)                         |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)             |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |   85x85 | 40000   | 1.028    |              - | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json)             |
+|  UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
+|  UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.010    |              - | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
 
 ### CHASE_DB1
 
 | Method    | Backbone  | Loss    | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  mDice | Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-|  FCN |   UNet-S5-D16   | Cross Entropy   | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - |  89.46 |80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                         |
-|  FCN |   UNet-S5-D16   | Cross Entropy + Dice   | 960x999    | 128x128   |  85x85 | 40000   | 0.986    |              - | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json)                         |
-|  PSPNet | UNet-S5-D16  | Cross Entropy  | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - |  89.52 |80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py)    | [model](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) &#124; [log](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.log.json)             |
-|  PSPNet | UNet-S5-D16  | Cross Entropy + Dice  | 960x999    | 128x128   |  85x85 | 40000   | 1.028    |              - | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json)             |
-| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - |  89.57 |80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](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) &#124; [log](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.log.json) |
-| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.010    |              - | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
+|  UNet + FCN |   UNet-S5-D16   | Cross Entropy   | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - |  89.46 |80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                         |
+|  UNet + FCN |   UNet-S5-D16   | Cross Entropy + Dice   | 960x999    | 128x128   |  85x85 | 40000   | 0.986    |              - | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json)                         |
+|  UNet + PSPNet | UNet-S5-D16  | Cross Entropy  | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - |  89.52 |80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py)    | [model](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) &#124; [log](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.log.json)             |
+|  UNet + PSPNet | UNet-S5-D16  | Cross Entropy + Dice  | 960x999    | 128x128   |  85x85 | 40000   | 1.028    |              - | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json)             |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - |  89.57 |80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](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) &#124; [log](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.log.json) |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.010    |              - | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
 
 ### HRF
 
 |  Method    | Backbone  | Loss    | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  mDice | Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-|  FCN |    UNet-S5-D16  | Cross Entropy  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - |  88.92 |79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                         |
-|  FCN |    UNet-S5-D16  | Cross Entropy + Dice  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.623    |              - | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py)       | [model](https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json)                         |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - |  89.24 |80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)             |
-|  PSPNet |  UNet-S5-D16 | Cross Entropy + Dice  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.798    |              - | 89.69 |  80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json)             |
-|  DeepLabV3 | UNet-S5-D16| Cross Entropy | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - |  89.32 |80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
-|  DeepLabV3 | UNet-S5-D16| Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.607    |              - | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
+|  UNet + FCN |    UNet-S5-D16  | Cross Entropy  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - |  88.92 |79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py)       | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                         |
+|  UNet + FCN |    UNet-S5-D16  | Cross Entropy + Dice  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.623    |              - | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py)       | [model](https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json)                         |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - |  89.24 |80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py)    | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)             |
+|  UNet + PSPNet |  UNet-S5-D16 | Cross Entropy + Dice  | 2336x3504  | 256x256   | 170x170 | 40000   | 2.798    |              - | 89.69 |  80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json)             |
+|  UNet + DeepLabV3 | UNet-S5-D16| Cross Entropy | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - |  89.32 |80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
+|  UNet + DeepLabV3 | UNet-S5-D16| Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.607    |              - | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
 
 Note:
 
diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml
index 4edafc5..2918e5a 100644
--- a/configs/unet/unet.yml
+++ b/configs/unet/unet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: unet
+- Name: UNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -18,7 +18,7 @@ Collections:
     Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
 Models:
 - Name: fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (512,1024)
@@ -40,7 +40,7 @@ Models:
   Config: configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
 - Name: fcn_unet_s5-d16_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -54,7 +54,7 @@ Models:
   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
 - Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -68,7 +68,7 @@ Models:
   Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
 - Name: pspnet_unet_s5-d16_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -96,7 +96,7 @@ Models:
   Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
 - Name: deeplabv3_unet_s5-d16_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -110,7 +110,7 @@ Models:
   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
 - Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (64,64)
@@ -124,7 +124,7 @@ Models:
   Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
 - Name: fcn_unet_s5-d16_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -138,7 +138,7 @@ Models:
   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
 - Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -152,7 +152,7 @@ Models:
   Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
 - Name: pspnet_unet_s5-d16_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -166,7 +166,7 @@ Models:
   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
 - Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -180,7 +180,7 @@ Models:
   Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
 - Name: deeplabv3_unet_s5-d16_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -194,7 +194,7 @@ Models:
   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
 - Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -208,7 +208,7 @@ Models:
   Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
 - Name: fcn_unet_s5-d16_128x128_40k_chase_db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -222,7 +222,7 @@ Models:
   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
 - Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -236,7 +236,7 @@ Models:
   Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
 - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -250,7 +250,7 @@ Models:
   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
 - Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -264,7 +264,7 @@ Models:
   Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
 - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -278,7 +278,7 @@ Models:
   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
 - Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (128,128)
@@ -292,7 +292,7 @@ Models:
   Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
 - Name: fcn_unet_s5-d16_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
@@ -306,7 +306,7 @@ Models:
   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
 - Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
@@ -320,7 +320,7 @@ Models:
   Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
   Weights: https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
 - Name: pspnet_unet_s5-d16_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
@@ -334,7 +334,7 @@ Models:
   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
 - Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
@@ -348,7 +348,7 @@ Models:
   Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
 - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
@@ -362,7 +362,7 @@ Models:
   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
 - Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
-  In Collection: unet
+  In Collection: UNet
   Metadata:
     backbone: UNet-S5-D16
     crop size: (256,256)
diff --git a/configs/upernet/upernet.yml b/configs/upernet/upernet.yml
index a923743..7c3872a 100644
--- a/configs/upernet/upernet.yml
+++ b/configs/upernet/upernet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: upernet
+- Name: UPerNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/CSAILVision/unifiedparsing
 Models:
 - Name: upernet_r50_512x1024_40k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   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
 - Name: upernet_r101_512x1024_40k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   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
 - Name: upernet_r50_769x769_40k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   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
 - Name: upernet_r101_769x769_40k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   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
 - Name: upernet_r50_512x1024_80k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   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
 - Name: upernet_r101_512x1024_80k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   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
 - Name: upernet_r50_769x769_80k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   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
 - Name: upernet_r101_769x769_80k_cityscapes
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   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
 - Name: upernet_r50_512x512_80k_ade20k
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   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
 - Name: upernet_r101_512x512_80k_ade20k
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   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
 - Name: upernet_r50_512x512_160k_ade20k
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   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
 - Name: upernet_r101_512x512_160k_ade20k
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   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
 - Name: upernet_r50_512x512_20k_voc12aug
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   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
 - Name: upernet_r101_512x512_20k_voc12aug
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   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
 - Name: upernet_r50_512x512_40k_voc12aug
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-50
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   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
 - Name: upernet_r101_512x512_40k_voc12aug
-  In Collection: upernet
+  In Collection: UPerNet
   Metadata:
     backbone: R-101
     crop size: (512,512)
diff --git a/configs/vit/vit.yml b/configs/vit/vit.yml
index 9e3b02e..35e4952 100644
--- a/configs/vit/vit.yml
+++ b/configs/vit/vit.yml
@@ -1,20 +1,6 @@
-Collections:
-- Name: vit
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/pdf/2010.11929.pdf
-    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
-  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:
 - Name: upernet_vit-b16_mln_512x512_80k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: ViT-B + MLN
     crop size: (512,512)
@@ -36,7 +22,7 @@ Models:
   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
 - Name: upernet_vit-b16_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: ViT-B + MLN
     crop size: (512,512)
@@ -58,7 +44,7 @@ Models:
   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
 - Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: ViT-B + LN + MLN
     crop size: (512,512)
@@ -80,7 +66,7 @@ Models:
   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
 - Name: upernet_deit-s16_512x512_80k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-S
     crop size: (512,512)
@@ -102,7 +88,7 @@ Models:
   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
 - Name: upernet_deit-s16_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-S
     crop size: (512,512)
@@ -124,7 +110,7 @@ Models:
   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
 - Name: upernet_deit-s16_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-S + MLN
     crop size: (512,512)
@@ -146,7 +132,7 @@ Models:
   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
 - Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-S + LN + MLN
     crop size: (512,512)
@@ -168,7 +154,7 @@ Models:
   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
 - Name: upernet_deit-b16_512x512_80k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-B
     crop size: (512,512)
@@ -190,7 +176,7 @@ Models:
   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
 - Name: upernet_deit-b16_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-B
     crop size: (512,512)
@@ -212,7 +198,7 @@ Models:
   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
 - Name: upernet_deit-b16_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-B + MLN
     crop size: (512,512)
@@ -234,7 +220,7 @@ Models:
   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
 - Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
-  In Collection: vit
+  In Collection: UPerNet
   Metadata:
     backbone: DeiT-B + LN + MLN
     crop size: (512,512)