live-group/VCFL

View Confusion Feature Learning

Related Projects:

• Strong Triplet Loss Baseline
• Strong Identification Loss Baseline
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification using pytorch.

If you adopt VCFL in your research, please cite the paper

@InProceedings{Liu_2019_ICCV,
author = {Liu, Fangyi and Zhang, Lei},
title = {View Confusion Feature Learning for Person Re-Identification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

TODO List

• Model
  • ResNet-50
• Loss
  • Triplet Global Loss
  • Center Loss
  • Sift Loss
  • Confusion Loss
• Testing
  • Re-Ranking

Installation

It's recommended that you create and enter a python virtual environment, if versions of the packages required here conflict with yours.

I use Python 2.7 and Pytorch 0.3. For installing Pytorch, follow the official guide. Other packages are specified in requirements.txt.

pip install -r requirements.txt

Our code is partly based on AlignedReID-Re-Production-Pytorch, you can clone the repository:

git clone https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch.git
cd AlignedReID-Re-Production-Pytorch

Our code can clone the repository

git clone https://github.com/fountaindream/VCFL.git
cd VCFL

Dataset Preparation

Inspired by Tong Xiao's open-reid project, dataset directories are refactored to support a unified dataset interface.

Transformed dataset has following features

• All used images, including training and testing images, are inside the same folder named images
• Images are renamed, with the name mapping from original images to new ones provided in a file named ori_to_new_im_name.pkl. The mapping may be needed in some cases.
• The train/val/test partitions are recorded in a file named partitions.pkl which is a dict with the following keys
  • 'trainval_im_names'
  • 'trainval_ids2labels'
  • 'train_im_names'
  • 'train_ids2labels'
  • 'val_im_names'
  • 'val_marks'
  • 'test_im_names'
  • 'test_marks'
• Validation set consists of 100 persons (configurable during transforming dataset) unseen in training set, and validation follows the same ranking protocol of testing.
• Each val or test image is accompanied by a mark denoting whether it is from
  • query (mark == 0), or
  • gallery (mark == 1), or
  • multi query (mark == 2) set

Market1501

You can download 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

CUHK03

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 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

DukeMTMC-reID

You can download 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

Configure Dataset Path

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

# In file basic_vcfl/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')

Evaluation Protocol

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 basic_vcfl/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)}

Examples

ResNet-50 on Market1501

You can train with different scripts. Train_confusion（only include confusion loss）,Train_sift(only include sift loss),Train_cen(only include center loss)

--------------------------------Training------------------------------------

python script/experiment/train_whole.py \
-d '(0,)' \
-r 1 \
--dataset market1501 \
--ids_per_batch 32 \
--ims_per_id 4 \
--normalize_feature false \
-gm 0.3 \
-glw 1 \
-llw 0 \
-idlw 0 \
-slw 0.1 \
-clw 0.0001 \
-dglw 0 \
--base_lr 3e-4 \
--lr_decay_type exp \
--exp_decay_at_epoch 151 \
--total_epochs 300

----------------------------testing with trained model-----------------------

python script/experiment/train_whole.py \
-d '(0,)' \
--dataset market1501 \
--normalize_feature false \
-glw 1 \
-llw 0 \
-idlw 0 \
--only_test true \
--exp_dir SPECIFY_AN_EXPERIMENT_DIRECTORY_HERE \
--model_weight_file THE_DOWNLOADED_MODEL_WEIGHT_FILE

Log

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

References & Credits

• AlignedReID
• open-reid
• In Defense of the Triplet Loss for Person Re-Identification
• Re-ranking Person Re-identification with k-reciprocal Encoding
• Market1501
• CUHK03
• DukeMTMC-reID