sanchitprakash/DG-Net

Joint Discriminative and Generative Learning for Person Re-identification

Joint Discriminative and Generative Learning for Person Re-identification
Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, Jan Kautz
In CVPR 2019 (Oral).

Features

We have supported:

Float16 to save GPU memory based on APEX
Multiple query evaluation
Random erasing
Visualize training curves
Generate all figures in the paper

License

The code is released for academic research use only. For commercial use, please contact researchinquiries@nvidia.com.

Prerequisites

Python 3.6
GPU Memory >= 15G
GPU Memory >= 10G (for fp16)
NumPy
PyTorch 1.0+
[Optional] APEX (for fp16)

Getting Started

Installation

Install PyTorch from http://pytorch.org/
Install TorchVision from the source:

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

[Optinal] You may skip it. Install APEX from the source:

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

Clone this repo.

git clone https://github.com/NVlabs/DG-Net.git
cd DG-Net/

Our code is tested on PyTorch 1.0.0+ and TorchVision 0.2.1+ .

Dataset Preparation

Download the dataset Market-1501

Preparation: put the images with the same id in one folder. You may use

python prepare-market.py          # for Market-1501

Note to modify the dataset path to your own path.

Testing

Download the trained model

We provide our trained model. You may download and move it to the outputs.

├── outputs/
│   ├── E0.5new_reid0.5_w30000
├── models
│   ├── best/

Person re-id evaluation

Please check the README.md in the ./reid_eval.

Image generation evaluation

Please check the README.md in the ./visual_tools.

You may use the ./visual_tools/test_folder.py to generate lots of images and then do the evaluation. The only thing you need to modify is the data path.

SSIM https://github.com/layumi/PerceptualSimilarity
FID https://github.com/layumi/TTUR (To evaluate, you need to install tensorflow-gpu)

Training

Train a teacher model

You may directly download our trained teacher model from trained model. If you want to have it trained by yourself, please check the person re-id baseline repository to train a teacher model, then copy and put it in the ./models.

├── models/
│   ├── best/                   /* teacher Model for Market-1501
│       ├── net_last.pth        /* model file
│       ├── ...

Train DG-Net

Setup the yaml file. Check out configs/latest.yaml. Change the data_root field to the path of your prepared folder-based dataset, e.g. ../Market-1501/pytorch.
Start training

python train.py --config configs/latest.yaml

Or train with low precision (fp16)

python train.py --config configs/latest-fp16.yaml

Intermediate image outputs and model binary files are saved in outputs/latest.

Check the loss log

 tensorboard --logdir logs/latest

Tips

Note the format of the camera id and the number of cameras.

For some datasets (e.g., MSMT17), there are more than 10 cameras. You need to modify the prepariation and evaluation code to read the double-digit camera id.

For some vehicle re-id datasets (e.g., VeRi) having different naming rules, you also need to modify the preparition and evaluation code.

Citation

Please cite this paper if it helps your research:

@article{zheng2019joint,
  title={Joint discriminative and generative learning for person re-identification},
  author={Zheng, Zhedong and Yang, Xiaodong and Yu, Zhiding and Zheng, Liang and Yang, Yi and Kautz, Jan},
  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

Related Work

Other GAN-based methods compared in the paper: we forked the code and made some changes for evaluatation. Thank authors for their great work.

We would also like to thank to the great projects in person re-id baseline, MUNIT and DRIT.

About

Joint Discriminative and Generative Learning for Person Re-identification (CVPR 2019 Oral)

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sanchitprakash / DG-Net