Add tag to README in configs (#262)

* Add Tag to configs

* Add Tag to configs

* Add Tag to config

configs/3dssd/README.md

 # 3DSSD: Point-based 3D Single Stage Object Detector
 
 ## Introduction
+
+[ALGORITHM]
+
 We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
 
 ```

configs/centerpoint/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement CenterPoint and provide the result and checkpoints on nuScenes dataset.
 
 We follow the below style to name config files. Contributors are advised to follow the same style.

configs/dynamic_voxelization/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement Dynamic Voxelization proposed in  and provide its results and models on KITTI dataset.
 ```
 @article{zhou2019endtoend,

configs/fp16/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[OTHERS]
+
 We implement mixed precision training and apply it to VoxelNets (e.g., SECOND and PointPillars).
 The results are in the following tables.
 

configs/free_anchor/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement FreeAnchor in 3D detection systems and provide their first results with PointPillars on nuScenes dataset.
 With the implemented `FreeAnchor3DHead`, a PointPillar detector with a big backbone (e.g., RegNet-3.2GF) achieves top performance
 on the nuScenes benchmark.

configs/h3dnet/README.md

 # H3DNet: 3D Object Detection Using Hybrid Geometric Primitives
 
 ## Introduction
+
+[ALGORITHM]
+
 We implement H3DNet and provide the result and checkpoints on ScanNet datasets.
 ```
 @inproceedings{zhang2020h3dnet,

configs/mvxnet/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement MVX-Net and provide its results and models on KITTI dataset.
 ```
 @inproceedings{sindagi2019mvx,

configs/nuimages/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[DATASET]
+
 We support and provide some baseline results on [nuImages dataset](https://www.nuscenes.org/nuimages).
 We follow the class mapping in nuScenes dataset, which maps the original categories into 10 foreground categories.
 The baseline results include instance segmentation models, e.g., Mask R-CNN and Cascade Mask R-CNN.

configs/parta2/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement Part-A^2 and provide its results and checkpoints on KITTI dataset.
 
 ```

configs/pointpillars/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement PointPillars and provide the results and checkpoints on KITTI, nuScenes, Lyft and Waymo datasets.
 
 ```

configs/regnet/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[BACKBONE]
+
 We implement RegNetX models in 3D detection systems and provide their first results with PointPillars on nuScenes dataset.
 
 The pre-trained modles are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md) and maintained in [mmcv](https://github.com/open-mmlab/mmcv).

configs/second/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement SECOND and provide the results and checkpoints on KITTI dataset.
 ```
 @article{yan2018second,

configs/ssn/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement PointPillars with Shape-aware grouping heads used in the SSN and provide the results and checkpoints on the nuScenes and Lyft dataset.
 
 ```

configs/votenet/README.md

@@ -2,6 +2,8 @@
 
 ## Introduction
 
+[ALGORITHM]
+
 We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRGBD datasets.
 
 ```