Add Tag to config (open-mmlab#4426)

configs/albu_example/README.md

@@ -1,5 +1,7 @@
 # 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

@@ -2,6 +2,8 @@
 
 ## 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{Cai_2019,
    title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},

configs/cascade_rpn/README.md

@@ -1,5 +1,7 @@
 # Cascade RPN
 
+[ALGORITHM]
+
 We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
 
 ```

configs/centripetalnet/README.md

@@ -2,6 +2,8 @@
 
 ## 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

@@ -1,5 +1,7 @@
 # Cityscapes Dataset
 
+[DATASET]
+
 ## 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{law2018cornernet,
   title={Cornernet: Detecting objects as paired keypoints},

configs/dcn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```none
 @inproceedings{dai2017deformable,
   title={Deformable Convolutional Networks},

configs/deepfashion/README.md

@@ -1,5 +1,7 @@
 # DeepFashion
 
+[DATASET]
+
 [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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{wu2019rethinking,
     title={Rethinking Classification and Localization for Object Detection},

configs/dynamic_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```
 @article{DynamicRCNN,
     author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen},

configs/empirical_attention/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{zhu2019empirical,
   title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks},

configs/fast_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{girshick2015fast,
   title={Fast r-cnn},

configs/faster_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{Ren_2017,
    title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},

configs/fcos/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{tian2019fcos,
   title={FCOS: Fully Convolutional One-Stage Object Detection},

configs/foveabox/README.md

@@ -1,5 +1,7 @@
 # FoveaBox: Beyond Anchor-based Object Detector
 
+[ALGORITHM]
+
 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

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{micikevicius2017mixed,
   title={Mixed precision training},

configs/free_anchor/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{zhang2019freeanchor,
   title   =  {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection},

configs/fsaf/README.md

@@ -1,5 +1,7 @@
 # Feature Selective Anchor-Free Module for Single-Shot Object Detection
 
+[ALGORITHM]
+
 FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)).
 Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level.
 And this is how we implemented it.

configs/gcnet/README.md

@@ -7,6 +7,8 @@ We provide config files to reproduce the results in the paper for
 
 ## Introduction
 
+[ALGORITHM]
+
 **GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet),  GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks.
 
 ## Citing GCNet

configs/gfl/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388)
 
 ```latex

configs/ghm/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```
 @inproceedings{li2019gradient,
   title={Gradient Harmonized Single-stage Detector},

configs/gn+ws/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```
 @article{weightstandardization,
   author    = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},

configs/gn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{wu2018group,
   title={Group Normalization},

configs/grid_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{lu2019grid,
   title={Grid r-cnn},

configs/groie/README.md

@@ -11,6 +11,8 @@ on COCO object detection.
 
 ## Introduction
 
+[ALGORITHM]
+
 This paper is motivated by the need to overcome to the limitations of existing
 RoI extractors which select only one (the best) layer from FPN.
 

configs/guided_anchoring/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278).
 
 ```latex

configs/hrnet/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[BACKBONE]
+
 ```latex
 @inproceedings{SunXLW19,
   title={Deep High-Resolution Representation Learning for Human Pose Estimation},

configs/htc/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518).
 
 ```latex

configs/instaboost/README.md

@@ -1,5 +1,7 @@
 # InstaBoost for MMDetection
 
+[ALGORITHM]
+
 Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801).
 
 ```latex

configs/legacy_1.x/README.md

@@ -1,5 +1,7 @@
 # Legacy Configs in MMDetection V1.x
 
+[OTHERS]
+
 Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos.
 
 To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models.

configs/libra_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf).
 
 ```

configs/lvis/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[DATASET]
+
 ```latex
 @inproceedings{gupta2019lvis,
   title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},

configs/mask_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{He_2017,
    title={Mask R-CNN},

configs/ms_rcnn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```
 @inproceedings{huang2019msrcnn,
     title={Mask Scoring R-CNN},

configs/nas_fcos/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @article{wang2019fcos,
   title={Nas-fcos: Fast neural architecture search for object detection},

configs/nas_fpn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{ghiasi2019fpn,
   title={Nas-fpn: Learning scalable feature pyramid architecture for object detection},

configs/paa/README.md

@@ -1,5 +1,7 @@
 # Probabilistic Anchor Assignment with IoU Prediction for Object Detection
 
+[ALGORITHM]
+
 ## Results and Models
 
 We provide config files to reproduce the object detection results in the

configs/pafpn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```
 @inproceedings{liu2018path,
   author = {Shu Liu and

configs/pascal_voc/README.md

@@ -1,5 +1,7 @@
 # PASCAL VOC Dataset
 
+[DATASET]
+
 ## Results and Models
 
 | Architecture | Backbone  | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |

configs/pisa/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @inproceedings{cao2019prime,
   title={Prime sample attention in object detection},

configs/point_rend/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 ```latex
 @InProceedings{kirillov2019pointrend,
   title={{PointRend}: Image Segmentation as Rendering},