2.6 KiB

Raw Blame History

Bag of Tricks and A Strong ReID Baseline in FastReID

Bag of Tricks and A Strong Baseline for Deep Person Re-identification. CVPRW2019, Oral.

Hao Luo* Youzhi Gu* Xingyu Liao* Shenqi Lai

A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification. IEEE Transactions on Multimedia (Accepted).

[Journal Version(TMM)] [PDF] [Slides] [Poster]

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/bagtricks_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

You can reproduce the results by simply excute

sh scripts/train_market.sh
sh scripts/train_duke.sh
sh scripts/train_msmt.sh

Market1501 dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	93.9%	84.9%	57.1%

DukeMTMC dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	87.1%	76.4%	39.2%

MSMT17 dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	72.2%	48.4%	9.6%

@InProceedings{Luo_2019_CVPR_Workshops,
author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
title = {Bag of Tricks and a Strong Baseline for Deep Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

@ARTICLE{Luo_2019_Strong_TMM, 
author={H. {Luo} and W. {Jiang} and Y. {Gu} and F. {Liu} and X. {Liao} and S. {Lai} and J. {Gu}}, 
journal={IEEE Transactions on Multimedia}, 
title={A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification}, 
year={2019}, 
pages={1-1}, 
doi={10.1109/TMM.2019.2958756}, 
ISSN={1941-0077}, 
}

2.6 KiB Raw Blame History