Update README.md

Author: layumi

leaderboard/README.md

@@ -14,6 +14,7 @@ Priorities are given to papers whose codes are published.
  :helicopter:  Drone-based building re-id [[code]](https://github.com/layumi/University1652-Baseline)  [[paper]](https://arxiv.org/abs/2002.12186)
 
  ## Supervised Learning
+ 
  Train and Test on DukeMTMC-reID
 
 |Methods | Rank@1 | mAP| Reference|
@@ -103,13 +104,15 @@ Is it possible to use less annotation on the unseen dataset, especially ID label
 | UDARTP | :heavy_check_mark: | 75.0% | 57.1% | "[Unsupervised Domain Adaptive Re-Identification: Theory and Practice](https://arxiv.org/pdf/1807.11334.pdf)", Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, and Xinggang Wang, arXiv:1807.11334|
 
 ### Train on [MSMT17](https://www.pkuvmc.com/publications/msmt17.html), Test on Market
+
 |Methods | Use Market Training Data (without ID label but may use the camera ID)| Rank@1 | mAP| Reference|
 | -------- | ----- | ----- | ---- | ---- |
 |OG-Net| :heavy_multiplication_x:|  40.1% | 17.6% | "[Parameter-Efficient Person Re-identification in the 3D Space](https://arxiv.org/abs/2006.04569)",  Zhedong Zheng and Yi Yang, TNNLS 2022. [**[pytorch code]**](https://github.com/layumi/person-reid-3d)| 
 | DG-Net | :heavy_multiplication_x:|  61.8% | 33.6% | "[Joint Discriminative and Generative Learning for Person Re-identification](https://arxiv.org/abs/1904.07223)",  Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang and Jan Kautz, CVPR 2019. [[code]](https://github.com/NVlabs/DG-Net) (Results are in Appendix)| 
 
 
 ## DukeMTMC-reID Protocol Citation
+
 ```bibtex
 @inproceedings{zheng2017unlabeled,
   title={Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro},