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Python 3 Pytorch 0.3

FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification


Yixiao Ge*, Zhuowan Li*, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, and Hongsheng Li
Neural Information Processing Systems (NIPS), 2018 (* equal contribution)

Pytorch implementation for our NIPS 2018 work. With the proposed siamese structure, we are able to learn identity-related and pose-unrelated representations.


  • Baidu Pan links of datasets and pretrained models have been updated.


  • Python 3
  • Pytorch (We run the code under version 0.3.1, maybe lower versions also work.)

Getting Started


  • Install dependencies (e.g., visdom and dominate). You can install all the dependencies by:
pip install scipy, pillow, torchvision, sklearn, h5py, dominate, visdom
  • Clone this repo:
git clone
cd FD-GAN/


We conduct experiments on Market1501, DukeMTMC-reID, CUHK03 datasets. We need pose landmarks for each dataset during training, so we generate the pose files by Realtime Multi-Person Pose Estimation. And the raw datasets have been preprocessed by the code in open-reid. Download the prepared datasets following below steps:

  • Create directories for datasets:
mkdir datasets
cd datasets/


As mentioned in the original paper, there are three stages for training our proposed framework.

Stage I: reID baseline pretraining

We use a Siamese baseline structure based on ResNet-50. You can train the model with follow commands,

python -b 256 -j 4 -d market1501 -a resnet50 --combine-trainval \
					--lr 0.01 --epochs 100 --step-size 40 --eval-step 5 \
					--logs-dir /path/to/save/checkpoints/

You can train it on specified GPUs by setting CUDA_VISIBLE_DEVICES, and change the dataset name [market1501|dukemtmc|cuhk03] after -d to train models on different datasets.
Or you can download the pretrained baseline model directly following the link below,

And test them with follow commands,

python -b 256 -d market1501 -a resnet50 --evaluate --resume /path/of/model_best.pth.tar

Stage II: FD-GAN pretraining

We need to pretain FD-GAN with the image encoder part (E in the original paper and net_E in the code) fixed first. You can train the model with follow commands,

python --display-port 6006 --display-id 1 \
	--stage 1 -d market1501 --name /directory/name/of/saving/checkpoints/ \
	--pose-aug gauss -b 256 -j 4 --niter 50 --niter-decay 50 --lr 0.001 --save-step 10 \
	--lambda-recon 100.0 --lambda-veri 0.0 --lambda-sp 10.0 --smooth-label \
	--netE-pretrain /path/of/model_best.pth.tar

You can train it on specified GPUs by setting CUDA_VISIBLE_DEVICES. For main arguments,

  • --display-port: display port of visdom, e.g., you can visualize the results by localhost:6006.
  • --display-id: set 0 to disable visdom.
  • --stage: set 1 for Stage II, and set 2 for stage III.
  • --pose-aug: choose from [no|erase|gauss] to make augmentations on pose maps.
  • --smooth-label: smooth the label of GANloss or not.

Other arguments can be viewed in Also you can directly download the models for stage II,

There are four models in each directory for separate nets.

Notice: If you use visdom for visualization by setting --display-id 1, you need to open a new window and run the script python -m visdom.server -port=6006 before running the main program, where -port should be consistent with --display-port.

Stage III: Global finetuning

Finetune the whole framework by optimizing all parts. You can train the model with follow commands,

python --display-port 6006 --display-id 1 \
	--stage 2 -d market1501 --name /directory/name/of/saving/checkpoints/ \
	--pose-aug gauss -b 256 -j 4 --niter 25 --niter-decay 25 --lr 0.0001 --save-step 10 --eval-step 5 \
	--lambda-recon 100.0 --lambda-veri 10.0 --lambda-sp 10.0 --smooth-label \
	--netE-pretrain /path/of/100_net_E.pth --netG-pretrain /path/of/100_net_G.pth \
	--netDi-pretrain /path/of/100_net_Di.pth --netDp-pretrain /path/of/100_net_Dp.pth

You can train it on specified GPUs by setting CUDA_VISIBLE_DEVICES.
We trained this model on a setting of batchsize 256. If you don't have such or better hardware, you may decrease the batchsize (the performance may also drop). Or you can directly download our final model,

And test best_net_E.pth by the same way as mentioned in Stage I.


  • scripts for generate pose landmarks.
  • generate specified images.


Please cite our paper if you find the code useful for your research.

  title={FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification},
  author={Ge, Yixiao and Li, Zhuowan and Zhao, Haiyu and Yin, Guojun and Yi, Shuai and Wang, Xiaogang and Li, Hongsheng},
  booktitle={Advances in Neural Information Processing Systems},


Our code is inspired by pytorch-CycleGAN-and-pix2pix and open-reid.


[NeurIPS-2018] FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification.






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