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Semi-Supervised Domain Generalizable Person Re-Identification (SSKD)

Introduction

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

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.

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.

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:
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

Results

Other some experimental results you could find in our [arxiv paper](https://arxiv.org/pdf/2108.05045.pdf).

Reference Project

Citation

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}
}

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Semi-supervised Knowledge Distillation

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