Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
JiahongX a060f1c317 | 1 year ago | |
---|---|---|
config | 1 year ago | |
datasets | 1 year ago | |
docs | 2 years ago | |
loss | 2 years ago | |
model | 2 years ago | |
pretrained_model | 2 years ago | |
processor | 2 years ago | |
solver | 2 years ago | |
tools | 2 years ago | |
utils | 2 years ago | |
LICENSE | 2 years ago | |
README.md | 2 years ago | |
info.txt | 2 years ago | |
test.py | 2 years ago | |
train.py | 1 year ago |
Paper: "Bag of Tricks and A Strong Baseline for Deep Person Re-identification"[pdf]
This project refers the official code link and can reproduce the results as good as it on Market1501 when the input size is set to 256x128. If you find this project useful, please cite the offical paper.
@inproceedings{luo2019bag,
title={Bag of Tricks and A Strong Baseline for Deep Person Re-identification},
author={Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
year={2019}
}
.
├── config # hyperparameters settings
│ └── ...
├── datasets # data loader
│ └── ...
├── log # log and model weights
├── loss # loss function code
│ └── ...
├── model # model
│ └── ...
├── processor # training and testing procedures
│ └── ...
├── solver # optimization code
│ └── ...
├── tools # tools
│ └── ...
├── utils # metrics code
│ └── ...
├── train.py # train code
├── test.py # test code
├── get_vis_result.py # get visualized results
├── docs # docs for readme
└── README.md
The pretrained (128x64) model can be downloaded now.
Extraction code is u3q5.
cd
to folder where you want to download this repo
Run git clone https://github.com/lulujianjie/person-reid-tiny-baseline.git
Install dependencies:
python train.py
python test.py
To get visualized reID results, first create results
folder in log dir, then:
python ./tools/get_vis_result.py
You will get the ranked results (query|rank1|rank2|...), like:
model | method | mAP | Rank1 |
---|---|---|---|
resnet50 | triplet loss + softmax + center loss (B1) | 85.8 | 94.1 |
resnet50 | B1 + flipped feature | 86.3 | 93.9 |
resnet50 | B1 + Harder Example Mining | 86.2 | 94.4 |
resnet50 | B1 + flipped feature + Harder Example Mining | 86.6 | 94.6 |
resnet50 | B1 + Harder Example Mining + reranking | 94.1 | 95.6 |
resnet50 | B1 + Harder Example Mining + searched reranking | 94.2 | 95.8 |
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》