Adaptive L2 Regularization in Person Re-Identification

Overview

We introduce an adaptive L2 regularization mechanism in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code is publicly available at https://github.com/nixingyang/AdaptiveL2Regularization.

Environment

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
conda config --set auto_activate_base false
conda create --yes --name TensorFlow2.2 python=3.8
conda activate TensorFlow2.2
conda install --yes cudatoolkit=10.1 cudnn=7.6 -c nvidia
conda install --yes cython matplotlib numpy=1.18 pandas pydot scikit-learn
pip install tensorflow==2.2.3
pip install opencv-python
pip install albumentations --no-binary imgaug,albumentations

Training

python3 -u solution.py --dataset_name "Market1501" --backbone_model_name "ResNet50"

• To train on other datasets, replace "Market1501" with "DukeMTMC_reID" or "MSMT17".
• To train with deeper backbones, replace "ResNet50" with "ResNet101" or "ResNet152".
• To evaluate on a subset of the complete test set, append --testing_size 0.5 to the command. Alternatively, you may turn this feature off by using --testing_size 0.0.

Evaluation

python3 -u solution.py --dataset_name "Market1501" --backbone_model_name "ResNet50" --pretrained_model_file_path "?.h5" --output_folder_path "evaluation_only" --evaluation_only --freeze_backbone_for_N_epochs 0 --testing_size 1.0 --evaluate_testing_every_N_epochs 1

• Fill in the pretrained_model_file_path argument using the h5 file obtained during training.
• To use the re-ranking method, append --use_re_ranking to the command.
• You need to run this separate evaluation procedure only if testing_size is not set to 1.0 during training.

Model Zoo

Acknowledgements

• Evaluation Metrics are adapted from deep-person-reid.
• Re-Ranking is adapted from person-re-ranking.
• Random Erasing is adapted from Random-Erasing.
• Triplet Loss is adapted from triplet-reid.

Third-Party Implementation

• The adaptive-l2-regularization-pytorch repository from duyuanchao in PyTorch.

Citation

Please consider citing this work if it helps your research.

@inproceedings{ni2021adaptive,
  author={Ni, Xingyang and Fang, Liang and Huttunen, Heikki},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  title={Adaptive L2 Regularization in Person Re-Identification},
  year={2021},
  volume={},
  number={},
  pages={9601-9607},
  doi={10.1109/ICPR48806.2021.9412481}
}