Introduction

This is the implementation codes for Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search

Overall pipeline of the proposed deep intra-image contrastive learning framework for weakly supervised one-step person search.

Installation

The project is based on MMdetection, please refer to install.md to install MMdetection.

We utilized cuda=11.3, pytorch=1.10.1, mmcv=1.2.6, mmdet=2.4.0

conda create -n dicl python=3.7 -y
conda activate dicl
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
cd mmcv
MMCV_WITH_OPS=1 pip install -e .
cd ..
pip install -r requirements/build.txt
pip install -v -e .
conda install -c conda-forge faiss=*=*_cuda
pip install  mmpycocotools

Dataset

Download CUHK-SYSU and PRW.

We provide coco-style annotation in demo/anno.

For CUHK-SYSU, change the path of your dataset and the annotaion file in the config file L2, L35, L40, L46, L51

For PRW, change the path of your dataset and the annotaion file in the config file L2, L35, L40, L46, L51

Experiments

1. Train

cd jobs/cuhk/
sh train.sh

2. Test CUHK-SYSU Download trained CUHK checkpoint. [loub]

cd jobs/cuhk/
sh test.sh

3. Train PRW

cd jobs/prw/
sh train.sh

4. Test PRW Download trained PRW checkpoint. [242a] Change the paths in L125 in test_results_prw.py

cd jobs/prw
sh test.sh

Performance

Dataset	Model	mAP	Rank1	Config	Link
CUHK-SYSU	DICL	87.4%	88.8%	cfg	model [loub]
PRW	DICL	35.5%	80.9%	cfg	model [242a]

Reference Codes

Thanks for the great projects of CGPS, MMdetection.

License

This project is released under the Apache 2.0 license.