fast-reid/projects/DG-ReID

• Semi-Supervised Domain Generalizable Person Re-Identification (SSKD)
• • Introduction
  • Dataset
  • Training & Evaluation
  • • Results
  • Reference Project
  • Citation

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 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

• fastreid
• centerX

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}
}