SSKD is implemented based on FastReID v1.0.0, it provides a semi-supervised feature learning framework to learn domain-general representations. The framework is shown in
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.
| 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) to obtain the human images.
The whole training process is divided into two stages:
- Train a student model (r34-ibn) and a teacher model (r101_ibn), you can run:
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:
python3 projects/SSKD/train_net.py --config-file projects/SSKD/configs/sskd.yml --num-gpu 4
If you use fastreid or sskd in your research, please give credit to the following papers:
@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}
}@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}
}