The implementation of the SCPNet modified from person-reid-triplet-loss-baseline. If you use this repo, please cite the following paper:

@inproceedings{SCPNet,
  title = {SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification},
  author = {Fan, Xing and Luo, Hao and Zhang, Xuan and He, Lingxiao and Zhang, Chi and Jiang, Wei},
  booktitle = {ACCV},
  year = {2018}
}

The original code is based on a internal deep learning framewok using 4 datasets together. We re-implement it using PyTorch in this repo based on person-reid-triplet-loss-baseline (More useful information may be found).

You can use Python2 and install PyTorch using the following commands (at least one Nvida GPU is required):

pip install torch==0.3.1
pip install torchvision

After training on single dataset alone for 200 epoch, the following results should be achieved:

Inspired by Tong Xiao's open-reid project, you need to prepare datasets first.

You can download what I have transformed for the project from Google Drive or BaiduYun. Otherwise, you can download the original dataset and transform it using my script, described below.

Download the Market1501 dataset from here. Run the following script to transform the dataset, replacing the paths with yours.

python script/dataset/transform_market1501.py \
--zip_file ~/Dataset/market1501/Market-1501-v15.09.15.zip \
--save_dir ~/Dataset/market1501

We follow the new training/testing protocol proposed in paper

@article{zhong2017re,
  title={Re-ranking Person Re-identification with k-reciprocal Encoding},
  author={Zhong, Zhun and Zheng, Liang and Cao, Donglin and Li, Shaozi},
  booktitle={CVPR},
  year={2017}
}

Details of the new protocol can be found here.

You can download what I have transformed for the project from Google Drive or BaiduYun. Otherwise, you can download the original dataset and transform it using my script, described below.

Download the CUHK03 dataset from here. Then download the training/testing partition file from Google Drive or BaiduYun. This partition file specifies which images are in training, query or gallery set. Finally run the following script to transform the dataset, replacing the paths with yours.

python script/dataset/transform_cuhk03.py \
--zip_file ~/Dataset/cuhk03/cuhk03_release.zip \
--train_test_partition_file ~/Dataset/cuhk03/re_ranking_train_test_split.pkl \
--save_dir ~/Dataset/cuhk03

You can download what I have transformed for the project from Google Drive or BaiduYun. Otherwise, you can download the original dataset and transform it using my script, described below.

Download the DukeMTMC-reID dataset from here. Run the following script to transform the dataset, replacing the paths with yours.

python script/dataset/transform_duke.py \
--zip_file ~/Dataset/duke/DukeMTMC-reID.zip \
--save_dir ~/Dataset/duke

Larger training set tends to benefit deep learning models, so I combine trainval set of three datasets Market1501, CUHK03 and DukeMTMC-reID. After training on the combined trainval set, the model can be tested on three test sets as usual.

Transform three separate datasets as introduced above if you have not done it.

For the trainval set, you can download what I have transformed from Google Drive or BaiduYun. Otherwise, you can run the following script to combine the trainval sets, replacing the paths with yours.

python script/dataset/combine_trainval_sets.py \
--market1501_im_dir ~/Dataset/market1501/images \
--market1501_partition_file ~/Dataset/market1501/partitions.pkl \
--cuhk03_im_dir ~/Dataset/cuhk03/detected/images \
--cuhk03_partition_file ~/Dataset/cuhk03/detected/partitions.pkl \
--duke_im_dir ~/Dataset/duke/images \
--duke_partition_file ~/Dataset/duke/partitions.pkl \
--save_dir ~/Dataset/market1501_cuhk03_duke

The project requires you to configure the dataset paths. In tri_loss/dataset/__init__.py, modify the following snippet according to your saving paths used in preparing datasets.

# In file tri_loss/dataset/__init__.py

########################################
# Specify Directory and Partition File #
########################################

if name == 'market1501':
  im_dir = ospeu('~/Dataset/market1501/images')
  partition_file = ospeu('~/Dataset/market1501/partitions.pkl')

elif name == 'cuhk03':
  im_type = ['detected', 'labeled'][0]
  im_dir = ospeu(ospj('~/Dataset/cuhk03', im_type, 'images'))
  partition_file = ospeu(ospj('~/Dataset/cuhk03', im_type, 'partitions.pkl'))

elif name == 'duke':
  im_dir = ospeu('~/Dataset/duke/images')
  partition_file = ospeu('~/Dataset/duke/partitions.pkl')

elif name == 'combined':
  assert part in ['trainval'], \
    "Only trainval part of the combined dataset is available now."
  im_dir = ospeu('~/Dataset/market1501_cuhk03_duke/trainval_images')
  partition_file = ospeu('~/Dataset/market1501_cuhk03_duke/partitions.pkl')

Datasets used in this project all follow the standard evaluation protocol of Market1501, using CMC and mAP metric. According to open-reid, the setting of CMC is as follows

# In file tri_loss/dataset/__init__.py

cmc_kwargs = dict(separate_camera_set=False,
                  single_gallery_shot=False,
                  first_match_break=True)

To play with different CMC options, you can modify it accordingly.

# In open-reid's reid/evaluators.py

# Compute all kinds of CMC scores
cmc_configs = {
  'allshots': dict(separate_camera_set=False,
                   single_gallery_shot=False,
                   first_match_break=False),
  'cuhk03': dict(separate_camera_set=True,
                 single_gallery_shot=True,
                 first_match_break=False),
  'market1501': dict(separate_camera_set=False,
                     single_gallery_shot=False,
                     first_match_break=True)}

My training log and saved model weights for three datasets can be downloaded from Google Drive or BaiduYun.

Specify

• a dataset name (one of market1501, cuhk03, duke)
• stride, 1 or 2
• an experiment directory for saving testing log
• the path of the downloaded model_weight.pth

in the following command and run it.

python2 script/experiment/train.py \
-d '(0,)' \
--only_test true \
--dataset DATASET_NAME \
--last_conv_stride STRIDE \
--normalize_feature false \
--exp_dir EXPERIMENT_DIRECTORY \
--model_weight_file THE_DOWNLOADED_MODEL_WEIGHT_FILE

You can also train it by yourself. The following command performs training, validation and finally testing automatically.

Specify

• a dataset name (one of ['market1501', 'cuhk03', 'duke'])
• stride, 1 or 2
• training on trainval set or train set (for tuning parameters)
• an experiment directory for saving training log

in the following command and run it.

python2 script/experiment/train.py \
-d '(0,)' \
--only_test false \
--dataset DATASET_NAME \
--last_conv_stride STRIDE \
--normalize_feature false \
--trainset_part TRAINVAL_OR_TRAIN \
--exp_dir EXPERIMENT_DIRECTORY \
--steps_per_log 10 \
--epochs_per_val 5

During training, you can run the TensorBoard and access port 6006 to watch the loss curves etc. E.g.

# Modify the path for `--logdir` accordingly.
tensorboard --logdir YOUR_EXPERIMENT_DIRECTORY/tensorboard

For more usage of TensorBoard, see the website and the help:

tensorboard --help

Specify

• a dataset name (one of ['market1501', 'cuhk03', 'duke'])
• stride, 1 or 2
• either model_weight_file (the downloaded model_weight.pth) OR ckpt_file (saved ckpt.pth during training)
• an experiment directory for saving images and log

in the following command and run it.

python script/experiment/visualize_rank_list.py \
-d '(0,)' \
--num_queries 16 \
--rank_list_size 10 \
--dataset DATASET_NAME \
--last_conv_stride STRIDE \
--normalize_feature false \
--exp_dir EXPERIMENT_DIRECTORY \
--model_weight_file '' \
--ckpt_file ''

Each query image and its ranking list would be saved to an image in directory EXPERIMENT_DIRECTORY/rank_lists. As shown in following examples, green boundary is added to true positive, and red to false positve.

• SCPNet

• person-reid-triplet-loss-baseline

• In Defense of the Triplet Loss for Person Re-Identification

• Beyond Part Models: Person Retrieval with Refined Part Pooling

• open-reid

• Re-ranking Person Re-identification with k-reciprocal Encoding

• Market1501

• CUHK03

• DukeMTMC-reID