Add Tag to config (#4426)

configs/albu_example/README.md

 # Albu Example
 
+[OTHERS]
+
 ## Results and Models
 
 | Backbone  | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |

configs/atss/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{zhang2019bridging,
   title   =  {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection},

configs/carafe/README.md

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 ## Introduction
 
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 We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188).
 
 ```

configs/cascade_rcnn/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{Cai_2019,
    title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},

configs/cascade_rpn/README.md

 # Cascade RPN
 
+[ALGORITHM]
+
 We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
 
 ```

configs/centripetalnet/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @InProceedings{Dong_2020_CVPR,
 author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen},

configs/cityscapes/README.md

 # Cityscapes Dataset
 
+[DATASET]
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 ## Common settings
 
 - All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate.

configs/cornernet/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{law2018cornernet,
   title={Cornernet: Detecting objects as paired keypoints},

configs/dcn/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```none
 @inproceedings{dai2017deformable,
   title={Deformable Convolutional Networks},

configs/deepfashion/README.md

 # DeepFashion
 
+[DATASET]
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 [MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module
 based on the dataset
 [DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing).

configs/detectors/README.md

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 ## Introduction
 
+[ALGORITHM]
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 We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).
 
 ```BibTeX

configs/detr/README.md

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 ## Introduction
 
+[ALGORITHM]
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 We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872).
 
 ```BibTeX

configs/double_heads/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{wu2019rethinking,
     title={Rethinking Classification and Localization for Object Detection},

configs/dynamic_rcnn/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```
 @article{DynamicRCNN,
     author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen},

configs/empirical_attention/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{zhu2019empirical,
   title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks},

configs/fast_rcnn/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{girshick2015fast,
   title={Fast r-cnn},

configs/faster_rcnn/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{Ren_2017,
    title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},

configs/fcos/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{tian2019fcos,
   title={FCOS: Fully Convolutional One-Stage Object Detection},

configs/foveabox/README.md

 # FoveaBox: Beyond Anchor-based Object Detector
 
+[ALGORITHM]
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 FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797):
 Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
 

configs/fp16/README.md

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 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{micikevicius2017mixed,
   title={Mixed precision training},