feat(projects): Add DG-ReID project

README.md

@@ -8,6 +8,7 @@ FastReID is a research platform that implements state-of-the-art re-identificati
 
 ## What's New
 
+- [Sep 2021] [DG-ReID](https://github.com/xiaomingzhid/sskd) is updated, you can check the [paper](https://arxiv.org/pdf/2108.05045.pdf).
 - [June 2021] [Contiguous parameters](https://github.com/PhilJd/contiguous_pytorch_params) is supported, now it can
   accelerate ~20%.
 - [May 2021] Vision Transformer backbone supported, see `configs/Market1501/bagtricks_vit.yml`.

projects/DG-ReID/README.md

@@ -0,0 +1,89 @@
+# Semi-Supervised Domain Generalizable Person Re-Identification (SSKD)
+
+## Introduction
+
+SSKD is implemented based on **FastReID v1.0.0**. You can refer to [sskd github link](https://github.com/xiaomingzhid/sskd) It provides a semi-supervised feature learning framework to learn domain-general representations. The framework is shown in 
+
+<img src="images/framework.png" width="850" >
+
+## Dataset
+
+**FastHuman** is very challenging, as it contains more complex application scenarios and large-scale training, testing datasets. It has diverse images from different application scenarios including campus, airport, shopping mall, street, and railway station.
+It contains 447,233 labeled images of 40,061 subjects captured by 82 cameras. The details of FastHuman, you can refer to [paper](https://arxiv.org/pdf/2108.05045.pdf).
+
+| Source Domain |  \#subjects | \#images | \#cameras | collection place |
+| ----- | :------: | :---------: | :----: | :------: |
+| CUHK03|  1,090 | 14,096 | 2 | campus |  
+| SAIVT | 152   | 7,150  | 8 | buildings |
+| AirportALERT | 9,651 | 30,243 | 6 | airport |
+|iLIDS|  300   | 4,515  | 2 | airport |
+|PKU  |  114   | 1,824  | 2 | campus |
+|PRAI |   1,580 | 39,481| 2 | aerial imagery |
+|SenseReID | 1,718 | 3,338  | 2 | unknown |
+|SYSU | 510  | 30,071 | 4 | campus |
+|Thermalworld | 409   | 8,103  | 1 | unknown |
+|3DPeS  | 193  | 1,012  | 1 | outdoor  |
+|CAVIARa | 72  | 1,220  | 1 | shopping mall |
+|VIPeR | 632   | 1,264  | 2 | unknown |
+|Shinpuhkan| 24 | 4,501  | 8 | unknown |
+|WildTrack | 313 | 33,979 | 7| outdoor |
+|cuhk-sysu | 11,934| 34,574 | 1| street |
+|LPW |  2,731 | 30,678 | 4 | street |
+|GRID |  1,025 | 1,275 | 8 | underground |
+|Total | 31,423| 246,049 | 57 | - |
+
+
+|Unseen Domain|  \#subjects | \#images | \#cameras | collection place  |
+| ----- | :------: | :---------: | :----: | :------: |
+|Market1501 | 1,501  | 32,217 | 6 | campus |
+|DukeMTMC | 1,812 | 36,441 | 8 | campus |
+|MSMT17 | 4,101 | 126,441| 15| campus |
+|PartialREID | 60 | 600| 6|campus |
+|PartialiLIDS | 119  | 238 | 2 | airport |
+|OccludedREID | 200  | 2,000| 5| campus |
+|CrowdREID | 845  | 3,257 | 11 | railway station| 
+|Total   | 8,638  | 201,184| 49 | - |
+
+**YouTube-Human** is a unlabeled human dataset. You can download the Street-View video from YouTube website, and the use the human detection algorithm ([centerX](https://github.com/JDAI-CV/centerX)) to obtain the human images.
+
+## Training & Evaluation
+
+The whole training process is divided into two stages:
+
+- Train a student model (r34-ibn) and a teacher model (r101_ibn), you can run:
+```bash
+python3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r34-ibn.yml --num-gpu 4
+python3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r101-ibn.yml --num-gpu 4
+```
+- Train the student model based unlabeled dataset and sskd, you can run:
+```bash
+python3 projects/SSKD/train_net.py --config-file projects/SSKD/configs/sskd.yml --num-gpu 4
+```
+### Results
+<img src="images/result1.png" width="550" >
+<img src="images/result2.png" width="500" >
+Other some experimental results you could find in our [arxiv paper](https://arxiv.org/pdf/2108.05045.pdf).
+
+## Reference Project
+- [fastreid](https://github.com/JDAI-CV/fast-reid)
+- [centerX](https://github.com/JDAI-CV/centerX)
+
+## Citation
+If you use **fastreid** or **sskd** in your research, please give credit to the following papers:
+
+```BibTeX
+@article{he2020fastreid,
+  title={FastReID: A Pytorch Toolbox for General Instance Re-identification},
+  author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},
+  journal={arXiv preprint arXiv:2006.02631},
+  year={2020}
+}
+```
+```BibTeX
+@article{he2021semi,
+  title={Semi-Supervised Domain Generalizable Person Re-Identification},
+  author={He, Lingxiao and Liu, Wu and Liang, Jian and Zheng, Kecheng and Liao, Xingyu and Cheng, Peng and Mei, Tao},
+  journal={arXiv preprint arXiv:2108.05045},
+  year={2021}
+}
+```

projects/README.md

@@ -14,7 +14,7 @@ Note that these are research projects, and therefore may not have the same level
 - [Attribute Recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastAttr)
 - [Hyper-Parameters Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastTune)
 - [Overhaul Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastDistill)
-- Semi-Supervised Domain Generalizable Person Re-Identification [Code](https://github.com/xiaomingzhid/sskd)[paper](https://arxiv.org/pdf/2108.05045.pdf)
+- Semi-Supervised Domain Generalizable Person Re-Identification. [code](https://github.com/xiaomingzhid/sskd) and [paper](https://arxiv.org/pdf/2108.05045.pdf)
 
 # External Projects