Pedestrian-Synthesis-GAN

See arxiv: https://arxiv.org/abs/1804.02047
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

Preparing

Prepare your data before training. The format of your data should follow the file in datasets.

Training stage

python train.py --dataroot data_path --name model_name --model pix2pix --which_model_netG unet_256 --which_direction BtoA --lambda_A 100 --dataset_mode aligned --use_spp --no_lsgan --norm batch

Testing stage

python test.py --dataroot data_path --name model_name --model pix2pix --which_model_netG unet_256 --which_direction BtoA  --dataset_mode aligned --use_spp --norm batch

Vision

Run python -m visdom.server to see the training process.

Citation

If you find this work useful for your research, please cite:

@article{ouyang2018pedestrian,
  title={Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond},
  author={Ouyang, Xi and Cheng, Yu and Jiang, Yifan and Li, Chun-Liang and Zhou, Pan},
  journal={arXiv preprint arXiv:1804.02047},
  year={2018}
}

Acknowledgments

Heavily borrow the code from pix2pix