Person Search

Introduction

A pytorch implementation for CVPR 2017 "Joint Detection and Identification Feature Learning for Person Search".

The code is based on the offcial caffe version.

Simpler code: After reduction and refactoring of the original code, the current version is simpler and easier to understand.
Pure Pytorch code: Numpy is not used, except for data reading.

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

torchvision must be greater than 0.3.0, as we need torchvision.ops.nms

Let's say $ROOT is the root directory.

Download CUHK-SYSU (google drive or baiduyun) dataset, unzip to $ROOT/data/dataset/
Download our trained model (google drive or baiduyun) (extraction code: uuti) to $ROOT/data/trained_model/

After the above two steps, the directory structure should look like this:

$ROOT/data
├── dataset
│   ├── annotation
│   ├── Image
│   └── README.txt
└── trained_model
    └── checkpoint_step_50000.pth

BTW, $ROOT/data saves all experimental data, include: dataset, pretrained model, trained model, and so on.

Run python tools/demo.py --gpu 0 --checkpoint data/trained_model/checkpoint_step_50000.pth. And then you can checkout the result in imgs directory.

Prepare dataset as we mentioned in Quick Start section.
Download pretrained model (google drive or baiduyun) (extraction code ucnw) to $ROOT/data/pretrained_model/
python tools/train_net.py --gpu 0
Trained model will be saved to $ROOT/data/trained_model/

You can check the usage of train_net.py by running python tools/train_net.py -h

python tools/test_net.py --gpu 0 --checkpoint data/trained_model/checkpoint_step_50000.pth

The result should be around:

Search ranking:
   mAP = 76.78%
   Top- 1 = 77.48%
   Top- 5 = 88.48%
   Top-10 = 91.52%