Domain Adaptive Person Search

Introduction

This is the official implementation for our paper Domain Adaptive Person Search (DAPS) in ECCV2022. The code is based on the official code of SeqNet and SPCL.

Performance : we tried some hyper-parameters and got better ReID performance reported in our paper.

Source	Target	mAP	Top-1	CKPT	log
PRW	CUHK-SYSU	78.5	80.7	ckpt	train_log
CUHK-SYSU	PRW	35.3	80.2	ckpt	train_log

Installation

run python setup.py develop to enable SPCL

Install Nvidia Apex

Run pip install -r requirements.txt in the root directory of the project.

Data Preparation

1. Download CUHK-SYSU and PRW datasets, and unzip them.
2. Modify configs/prw_da.yaml and configs/cuhk_sysu_da.yaml to change the dataset store place (Line 1,5,6) to your own path.

Testing

1. Following the link in the above table, download our pretrained model to anywhere you like

2. Evaluate its performance by specifing the paths of checkpoint and corresponding configuration file.

PRW as the target domain:

python train_da_dy_cluster.py --cfg configs/cuhk_sysu_da.yaml --eval --ckpt $MODEL_PATH

CUHK-SYSU as the target domain:

python train_da_dy_cluster.py --cfg configs/prw_da.yaml --eval --ckpt $MODEL_PATH

Training

PRW as the target domain:

python train_da_dy_cluster.py --cfg configs/cuhk_sysu_da.yaml

CUHK-SYSU as the target domain:

python train_da_dy_cluster.py --cfg configs/prw_da.yaml

Note: At present, our script only supports single GPU training, but distributed training will be also supported in future. By default, the batch size is set to 4, which requires about 27GB of GPU memory. If your GPU cannot provide the required memory, try smaller batch size and learning rate (performance may degrade).