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README.md

AGW Baseline in FastReID

Deep Learning for Person Re-identification: A Survey and Outlook. arXiv

This is a re-implementation of ReID-Survey with a Powerful AGW Baseline

Highlights

A comprehensive survey with in-depth analysis for person Re-ID in recent years (2016-2019).
A new evaluation metric, namely mean Inverse Negative Penalty (mINP), which measures the ability to find the hardest correct match.

Training

To train a model, run

CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <config.yml>

For example, to launch a end-to-end baseline training on market1501 dataset on GPU#1, one should excute:

CUDA_VISIBLE_DEVICES=1 python train_net.py --config-file='configs/AGW_market1501.yml'

Evaluation

To evaluate the model in test set, run similarly:

CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <configs.yaml> --eval-only MODEL.WEIGHTS model.pth

Experimental Results

Market1501 dataset

Method	Pretrained	Rank@1	mAP	mINP
AGW	ImageNet	94.9%	87.4%	63.1%

DukeMTMC dataset

Method	Pretrained	Rank@1	mAP	mINP
AGW	ImageNet	89.2%	79.5%	44.5%

MSMT17 dataset

Method	Pretrained	Rank@1	mAP	mINP
AGW	ImageNet	76.8%	53.7%	12.2%

@article{arxiv20reidsurvey,
  title={Deep Learning for Person Re-identification: A Survey and Outlook},
  author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H.},
  journal={arXiv preprint arXiv:2001.04193},
  year={2020},
}