[Doc] Update README.md in configs according to latest standard. (op…

…en-mmlab#1233) * fix README.md in configs * fix README.md in configs * modify [ALGORITHM] to [BACKBONE] in backbone config README.md
bowenroom · Jan 25, 2022 · 80e8504 · 80e8504
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diff --git a/.dev/md2yml.py b/.dev/md2yml.py
@@ -87,12 +87,13 @@ def parse_md(md_file):
         current_dataset = ''
         while i < len(lines):
             line = lines[i].strip()
+            # In latest README.md the title and url are in the third line.
+            if i == 2:
+                paper_url = lines[i].split('](')[1].split(')')[0]
+                paper_title = lines[i].split('](')[0].split('[')[1]
             if len(line) == 0:
                 i += 1
                 continue
-            if line[:2] == '# ':
-                paper_title = line.replace('# ', '')
-                i += 1
             elif line[:3] == '<a ':
                 content = etree.HTML(line)
                 node = content.xpath('//a')[0]
@@ -112,13 +113,6 @@ def parse_md(md_file):
                     assert repo_url is not None, (
                         f'{collection_name} hasn\'t official repo url.')
                 i += 1
-            elif line[:9] == '<summary ':
-                content = etree.HTML(line)
-                nodes = content.xpath('//a')
-                assert len(nodes) == 1, (
-                    'summary tag should only have single a tag.')
-                paper_url = nodes[0].get('href', None)
-                i += 1
             elif line[:4] == '### ':
                 datasets.append(line[4:])
                 current_dataset = line[4:]

diff --git a/README.md b/README.md
@@ -154,7 +154,7 @@ A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSe
 
 If you find this project useful in your research, please consider cite:
 
-```latex
+```bibtex
 @misc{mmseg2020,
     title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
     author={MMSegmentation Contributors},

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -153,7 +153,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMSegmentation。
 
-```latex
+```bibtex
 @misc{mmseg2020,
     title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
     author={MMSegmentation Contributors},

diff --git a/configs/ann/README.md b/configs/ann/README.md
@@ -1,4 +1,6 @@
-# Asymmetric Non-local Neural Networks for Semantic Segmentation
+# ANN
+
+[Asymmetric Non-local Neural Networks for Semantic Segmentation](https://arxiv.org/abs/1908.07678)
 
 ## Introduction
 
@@ -19,10 +21,10 @@ The non-local module works as a particularly useful technique for semantic segme
 <img src="https://user-images.githubusercontent.com/24582831/142898322-3bbd578c-e488-4bae-9c14-7598adac5cbd.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1908.07678">ANN (ICCV'2019)</a></summary>
 
-```latex
+## Citation
+
+```bibtex
 @inproceedings{zhu2019asymmetric,
   title={Asymmetric non-local neural networks for semantic segmentation},
   author={Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang},
@@ -32,7 +34,6 @@ The non-local module works as a particularly useful technique for semantic segme
 }
 ```
 
-</details>
 
 ## Results and models
 

diff --git a/configs/apcnet/README.md b/configs/apcnet/README.md
@@ -1,4 +1,6 @@
-# Adaptive Pyramid Context Network for Semantic Segmentation
+# APCNet
+
+[Adaptive Pyramid Context Network for Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ Recent studies witnessed that context features can significantly improve the per
 <img src="https://user-images.githubusercontent.com/24582831/142898638-e1c0c6ae-9270-448e-aa01-bbac3a236db5.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html">APCNet (CVPR'2019)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @InProceedings{He_2019_CVPR,
 author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu},
 title = {Adaptive Pyramid Context Network for Semantic Segmentation},
@@ -32,8 +33,6 @@ year = {2019}
 }
 ```
 
-</details>
-
 ## Results and models
 
 ### Cityscapes

diff --git a/configs/bisenetv1/README.md b/configs/bisenetv1/README.md
@@ -1,4 +1,6 @@
-# BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
+# BiSeNetV1
+
+[BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ Semantic segmentation requires both rich spatial information and sizeable recept
 <img src="https://user-images.githubusercontent.com/24582831/142898839-a0a78148-848a-41b2-8682-b1f61ac004ba.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1808.00897">BiSeNetV1 (ECCV'2018)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @inproceedings{yu2018bisenet,
   title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
   author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
@@ -32,8 +33,6 @@ Semantic segmentation requires both rich spatial information and sizeable recept
 }
 ```
 
-</details>
-
 ## Results and models
 
 ### Cityscapes

diff --git a/configs/bisenetv2/README.md b/configs/bisenetv2/README.md
@@ -1,4 +1,6 @@
-# Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
+# BiSeNetV2
+
+[Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://arxiv.org/abs/2004.02147)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ The low-level details and high-level semantics are both essential to the semanti
 <img src="https://user-images.githubusercontent.com/24582831/142898966-ec4a81da-b4b0-41ee-b083-1d964582c18a.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/2004.02147">BiSeNetV2 (IJCV'2021)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @article{yu2021bisenet,
   title={Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation},
   author={Yu, Changqian and Gao, Changxin and Wang, Jingbo and Yu, Gang and Shen, Chunhua and Sang, Nong},
@@ -33,7 +34,6 @@ The low-level details and high-level semantics are both essential to the semanti
 }
 ```
 
-</details>
 
 ## Results and models
 

diff --git a/configs/ccnet/README.md b/configs/ccnet/README.md
@@ -1,4 +1,6 @@
-# CCNet: Criss-Cross Attention for Semantic Segmentation
+# CCNet
+
+[CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ Contextual information is vital in visual understanding problems, such as semant
 <img src="https://user-images.githubusercontent.com/24582831/142899159-b329c12a-0fde-44df-8718-def6cfb004e4.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1811.11721">CCNet (ICCV'2019)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @article{huang2018ccnet,
     title={CCNet: Criss-Cross Attention for Semantic Segmentation},
     author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
@@ -31,7 +32,6 @@ Contextual information is vital in visual understanding problems, such as semant
 }
 ```
 
-</details>
 
 ## Results and models
 

diff --git a/configs/cgnet/README.md b/configs/cgnet/README.md
@@ -1,4 +1,6 @@
-# CGNet: A Light-weight Context Guided Network for Semantic Segmentation
+# CGNet
+
+[CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://arxiv.org/abs/1811.08201)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ The demand of applying semantic segmentation model on mobile devices has been in
 <img src="https://user-images.githubusercontent.com/24582831/142900351-89559574-79cc-4f57-8f69-5d88765ec38d.png" width="80%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/pdf/1811.08201.pdf">CGNet (TIP'2020)</a></summary>
+## Citation
 
-```latext
+```bibtext
 @article{wu2020cgnet,
   title={Cgnet: A light-weight context guided network for semantic segmentation},
   author={Wu, Tianyi and Tang, Sheng and Zhang, Rui and Cao, Juan and Zhang, Yongdong},
@@ -34,8 +35,6 @@ The demand of applying semantic segmentation model on mobile devices has been in
 }
 ```
 
-</details>
-
 ## Results and models
 
 ### Cityscapes

diff --git a/configs/cgnet/cgnet.yml b/configs/cgnet/cgnet.yml
@@ -4,7 +4,7 @@ Collections:
     Training Data:
     - Cityscapes
   Paper:
-    URL: https://arxiv.org/pdf/1811.08201.pdf
+    URL: https://arxiv.org/abs/1811.08201
     Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
   README: configs/cgnet/README.md
   Code:

diff --git a/configs/danet/README.md b/configs/danet/README.md
@@ -1,4 +1,6 @@
-# Dual Attention Network for Scene Segmentation
+# DANet
+
+[Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ In this paper, we address the scene segmentation task by capturing rich contextu
 <img src="https://user-images.githubusercontent.com/24582831/142900467-f832fdb9-3b7d-47d3-8e80-e6ee9303bdfb.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1809.02983">DANet (CVPR'2019)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @article{fu2018dual,
   title={Dual Attention Network for Scene Segmentation},
   author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
@@ -31,8 +32,6 @@ In this paper, we address the scene segmentation task by capturing rich contextu
 }
 ```
 
-</details>
-
 ## Results and models
 
 ### Cityscapes

diff --git a/configs/deeplabv3/README.md b/configs/deeplabv3/README.md
@@ -1,4 +1,6 @@
-# Rethinking atrous convolution for semantic image segmentation
+# DeepLabV3
+
+[Rethinking atrous convolution for semantic image segmentation](https://arxiv.org/abs/1706.05587)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ In this work, we revisit atrous convolution, a powerful tool to explicitly adjus
 <img src="https://user-images.githubusercontent.com/24582831/142900575-f30a7755-09aa-406a-bf78-45893a61ee9a.png" width="80%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1706.05587">DeepLabV3 (ArXiv'2017)</a></summary>
+## Citation
 
-```latext
+```bibtext
 @article{chen2017rethinking,
   title={Rethinking atrous convolution for semantic image segmentation},
   author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
@@ -31,14 +32,8 @@ In this work, we revisit atrous convolution, a powerful tool to explicitly adjus
 }
 ```
 
-</details>
-
 ## Results and models
 
-:::{note}
-`D-8` here corresponding to the output stride 8 setting for DeepLab series.
-:::
-
 ### Cityscapes
 
 | Method    | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                                                   |
@@ -117,4 +112,5 @@ In this work, we revisit atrous convolution, a powerful tool to explicitly adjus
 
 Note:
 
+- `D-8` here corresponding to the output stride 8 setting for DeepLab series.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md
@@ -1,4 +1,6 @@
-# Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+# DeepLabV3+
+
+[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
 <img src="https://user-images.githubusercontent.com/24582831/142900680-3e2c3098-8341-4760-bbfd-b1d7d29968ea.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://arxiv.org/abs/1802.02611">DeepLabV3+ (CVPR'2018)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @inproceedings{deeplabv3plus2018,
   title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
   author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
@@ -31,15 +32,8 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
 }
 ```
 
-</details>
-
 ## Results and models
 
-:::{note}
-`D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
-`MG-124` stands for multi-grid dilation in the last stage of ResNet.
-:::
-
 ### Cityscapes
 
 | Method     | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                                                           |
@@ -122,4 +116,6 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
 
 Note:
 
+- `D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
+- `MG-124` stands for multi-grid dilation in the last stage of ResNet.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md
@@ -1,4 +1,6 @@
-# Dynamic Multi-scale Filters for Semantic Segmentation
+# DMNet
+
+[Dynamic Multi-scale Filters for Semantic Segmentation](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)
 
 ## Introduction
 
@@ -19,10 +21,9 @@ Multi-scale representation provides an effective way toaddress scale variation o
 <img src="https://user-images.githubusercontent.com/24582831/142900781-6215763f-8b71-4e0b-a6b1-c41372db2aa0.png" width="70%"/>
 </div>
 
-<details>
-<summary align="right"><a href="https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf">DMNet (ICCV'2019)</a></summary>
+## Citation
 
-```latex
+```bibtex
 @InProceedings{He_2019_ICCV,
 author = {He, Junjun and Deng, Zhongying and Qiao, Yu},
 title = {Dynamic Multi-Scale Filters for Semantic Segmentation},
@@ -32,8 +33,6 @@ year = {2019}
 }
 ```
 
-</details>
-
 ## Results and models
 
 ### Cityscapes