This repository develops a baseline model with high performance, and implements the widely used baseline OIM [1], and NAE [4].
Clone repository and build the environment
git clone https://github.com/DeepAlchemist/deep-person-search.git && cd deep-person-search
conda env create -f env.yml
Download PRW and CUHK-SYSU (also named SSM)
to path_to_your_data
.
In the config file ./lib/cfg/config.py
, change --data_root
to path_to_your_data
,
--ckpt_dir
to path_you_want_to_save_the_checkpoints
.
Download ImageNet pre-trained ResNet models from GoogleDrive
to deep-person-search/cache/pretrained_model/
CUDA_VISIBLE_DEVICES=0 python main.py \
--benchmark ssm --batch_size 5 \
--backbone bsl --in_level C5 --cls_type oim \
--lr 0.003 --warmup_epochs 1 --max_epoch 7 \
--suffix ""
-dis
enable display (visualization), then tensorboard --bind_all --logdir your_log_dir
, which shows the loss curves and the input image with proposals.
CUDA_VISIBLE_DEVICES=0 python main.py --is_test \
--benchmark ssm --eval_batch_size 5 \
--backbone bsl --in_level C5 --cls_type oim \
--load_ckpt "absolute_path_to_your_checkpoint" \
CUHK-SYSU | PRW | |||
---|---|---|---|---|
Method | mAP | rank1 | mAP | rank1 |
OIM [1] | 88.1 | 89.2 | 36.0 | 76.7 |
NAE [4] | 89.8 | 90.7 | 37.9 | 77.3 |
baseline | 90.0 | 91.0 | 40.5 | 81.3 |
The download link of the trained models are available in the table. Note that all the models are trained with image size of 600x1000
, the larger image size, e.g., 900x1500
, would yield better performance.
[1] Joint Detection and Identification Feature Learning for Person Search. In CVPR 2017.
[2] Person Re-Identification in the Wild. In CVPR 2017.
[3] Query-guided End-to-End Person Search. In CVPR 2019.
[4] Norm-Aware Embedding for Efficient Person Search. In CVPR 2020.
@article{yang2021bottom,
title={Bottom-up foreground-aware feature fusion for practical person search},
author={Yang, Wenjie and Huang, Houjing and Chen, Xiaotang and Huang, Kaiqi},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={32},
number={1},
pages={262--274},
year={2021},
publisher={IEEE}
}