Unsupervised Pre-training for Person Re-identification (LUPerson)

Last update: Dec 24, 2022

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Deep Learning LUPerson

Overview

LUPerson

Unsupervised Pre-training for Person Re-identification (LUPerson).

The repository is for our CVPR2021 paper Unsupervised Pre-training for Person Re-identification.

LUPerson Dataset

LUPerson is currently the largest unlabeled dataset for Person Re-identification, which is used for Unsupervised Pre-training. LUPerson consists of 4M images of over 200K identities and covers a much diverse range of capturing environments.

Details can be found at ./LUP.

Pre-trained Models

Model	path
ResNet50	R50
ResNet101	R101
ResNet152	R152

Finetuned Results

For MGN with ResNet50:

Dataset	mAP	cmc1	path
MSMT17	66.06/79.93	85.08/87.63	MSMT
DukeMTMC	82.27/91.70	90.35/92.82	Duke
Market1501	91.12/96.16	96.26/97.12	Market
CUHK03-L	74.54/85.84	74.64/82.86	CUHK03

These numbers are a little different from those reported in our paper, and most are slightly better.

For MGN with ResNet101:

Dataset	mAP	cmc1	path
MSMT17	68.41/81.12	86.28/88.27	-
DukeMTMC	84.15/92.77	91.88/93.99	-
Market1501	91.86/96.21	96.56/97.03	-
CUHK03-L	75.98/86.73	75.86/84.07	-

The numbers are in the format of without RR/with RR.

Citation

If you find this code useful for your research, please cite our paper.

@article{fu2020unsupervised,
  title={Unsupervised Pre-training for Person Re-identification},
  author={Fu, Dengpan and Chen, Dongdong and Bao, Jianmin and Yang, Hao and Yuan, Lu and Zhang, Lei and Li, Houqiang and Chen, Dong},
  journal={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}

Comments

IBN ResNet pretraining

Hi Dengpan, Thanks for the great work! I am wondering if you are considering releasing pre-trained models with IBN-ResNet as backbones. It seems IBN is quite helpful for generalizable ReID, and perhaps even higher performance could be achieved with its help. (Or maybe you already did so -- that would be great too! In this case, do you mind sharing the models? That will be very valuable to me, thanks a lot!)

opened by Zhongdao 2
How can i get LUP/lmdbs/lmdb

In run_moco.sh ,There is --data_path "${DIR}/LUP/lmdbs/lmdb" however I can't found LUP/lmdbs/lmdb in my git clone. How can i get LUP/lmdbs/lmdb?

opened by vanmeruso 1
Regarding the dataset extraction

Hi @DengpanFu Thanks for your work. Can you please help me in figuring out the extraction of data from youtube videos as the dets.pkl don't contain the resolution images and every youtube video is different in res when extracted

opened by shreejalt 1
How to inference

Hi,

what a impressive concept from your paper

I found demo.py in fast-reid/demo/ for inference, but I can't ensure arguments when running : python demo.py --config-file [arg1] --parallel -- opts MODEL.WEIGHTS [arg2]

could you give a example ?

opened by swingbalance 1
Questions about YouTube video resoluton

Thanks for excellent jobs and sharing from the authors! But I want to know if anyone solved the problem of the resolution of a certain video that should be downloaded?

opened by YanzuoLu 0
About dataset downloading

Hi, thanks for your great work.

How can I download LUPerson dataset?

I can find LUPerson-NL dataset link but not the original LUPerson.

Could you provide the way?

opened by YangJae96 0
如何解析dets中的pkl

感谢您的工作，您dets中的pkl解析后每个item是如下所示格式，请问各个字段依此代表什么（比如1819代表？），另外这个检测信息如何知道对应到视频中的哪个帧？多谢． 1819: [[0, {'bbox': array([ 618.625, 81.75 , 1031.375, 709.25 ], dtype=float32), 'pose': array([[733.08105, 205.53418], [753.5075 , 176.93718], [720.8252 , 185.10774], [822.95734, 176.93718], [724.91046, 193.27832], [929.1748 , 328.09277], [716.7399 , 340.34863], [986.36884, 524.1865 ], [692.2282 , 462.90723], [790.2751 , 597.7217 ], [667.7165 , 528.2718 ], [855.63965, 695.76855], [716.7399 , 691.6833 ], [773.9339 , 720.2803 ], [761.67804, 691.6833 ], [896.4925 , 699.8538 ], [684.0576 , 703.93915]], dtype=float32), 'conf': 0.9488497, 'prob': array([[0.960111 ], [0.9625488 ], [0.96105295], [0.9494078 ], [0.5803897 ], [0.7805291 ], [0.8539633 ], [0.84015006], [0.9082806 ], [0.85463387], [0.9051482 ], [0.68101335], [0.77411926], [0.01756088], [0.01273601], [0.07441353], [0.05114838]], dtype=float32)}]]

opened by wang21jun 15
Errors in converting the torch models to ONNX

Following the steps mentioned in LUPerson/fast-reid/tools/deploy/ to export an ONNX file, and after running the below command (which I'm not 100% sure I got right):

!python onnx_export.py --config-file ../../configs/CMDM/mgn_R50_moco.yml --name "baseline_R50" --output outputs/onnx_model --opts MODEL.WEIGHTS market_mgn_r50.pth

I am getting the following error: AttributeError: module 'onnx' has no attribute 'optimizer'

Any help would be appreciated, and possibly a clarification as to which exact yml and pth files should be used in order to export the MGN model trained from LUPerson pre-trained networks.

opened by JosephAssaker 0

Owner

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