Code for ICCV 2019 Paper "Mixed High-Order Attention Network for Person Re-Identification"

This code is developed based on pytorch framework and the baseline code.

• Updates
• Files
• Prerequisites
• Train_Model
• Evaluation
• Contact
• Citation
• LICENSE

Updates

• Aug 16, 2019

  • The codes of training and testing for our ICCV19 paper are released.
  • We have cleared up and tested the codes on Market, Duke datasets, the expected retrieval performances are as follows:

  Market	R@1	R@5	R@10	mAP	Reference
  IDE+ERA	89.9%	96.4%	97.6%	75.6%	train_ide.py
  IDE+MHN6	93.1%	97.7%	98.7%	83.2%	train_ide.py
  PCB+ERA	91.7%	97.4%	98.3%	76.4%	train_smallPCB
  PCB+MHN4	94.3%	98.0%	98.8%	83.9%	train_smallPCB
  PCB+MHN6	94.8%	98.3%	98.9%	85.2%	train_smallPCB_multiGPU.py

  Duke	R@1	R@5	R@10	mAP	Reference
  IDE+ERA	82.7%	91.8%	94.1%	68.1%	train_ide.py
  IDE+MHN6	87.8%	94.2%	95.8%	74.6%	train_ide.py
  PCB+ERA	82.9%	91.7%	93.8%	67.7%	train_smallPCB
  PCB+MHN4	88.5%	94.5%	96.1%	76.9%	train_smallPCB
  PCB+MHN6	89.5%	94.7%	96.1%	77.5%	train_smallPCB_multiGPU.py

Files

• train_ide.py test_ide.py

  • files for training and testing on IDE framework

• train_smallPCB.py test_smallPCB.py

  • files for training and testing on PCB framework, when using MHN, the maximized order is limited to 4 due to the GPU memory.

• train_smallPCB_multiGPU.py test_smallPCB.py

  • files for training, if you want to test MHN6, please use this file for training with multi gpus. The testing file is also test_smallPCB.py

• auto_test.sh

  • auto-testing code.

    Prerequisites

• Pytorch(0.4.0+)

• python3.6

• 2GPUs, each > 11G

  Train_Model

  1. Clone our code.
  2. Download the training images {google drive, baidu}, including Market1501, DukeMTMC, CUHK03-NP.
  3. Go into the MHN/ dir and mkdir datasets/, then unzip the downloaded datasets.zip to datasets/
  4. Run prepare.py to preprocess the datasets.
  5. Then you can try our methods

     IDE+ERA

     python3 train_ide.py --gpu_ids 0 --name ide --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler

     IDE+MHN6

     python3 train_ide.py --gpu_ids 0 --name ide_mhn6 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 1.4 --parts 6 --mhn

     PCB+ERA

     python3 train_smallPCB.py --gpu_ids 0 --name pcb --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler

     PCB+MHN4

     python3 train_smallPCB.py --gpu_ids 0 --name pcb_mhn4 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 2 --parts 4 --mhn

     PCB+MHN6

     python3 train_smallPCB_multiGPU.py --gpu_ids 0,1 --name pcb_mhn6 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 2 --parts 6 --mhn

     the trained models are stored in folder "model/($name)".

     Evaluation

     We provide the auto-testing code in auto_test.sh, you can replace the corresponding code for testing. For example,

     For IDE+ERA

     python3 test_ide.py --gpu_ids $gpu_ids --name ide --test_dir datasets/Market/datasets/pytorch/ --batchsize 32 --which_epoch $i

     For IDE+MHN6

     python3 test_ide.py --gpu_ids $gpu_ids --name ide_mhn6 --test_dir datasets/Market/datasets/pytorch/ --batchsize 20 --which_epoch $i --mhn --parts 6

     For PCB+ERA

     python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb --test_dir datasets/Market/datasets/pytorch/ --batchsize 32 --which_epoch $i

     For PCB+MHN4

     python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb_mhn4 --test_dir datasets/Market/datasets/pytorch/ --batchsize 15 --which_epoch $i --mhn --parts 4

     For PCB+MHN6

     python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb_mhn6 --test_dir datasets/Market/datasets/pytorch/ --batchsize 10 --which_epoch $i --mhn --parts 6

     Contact

• Binghui Chen

Citation

You are encouraged to cite the following papers if this work helps your research.

@inproceedings{chen2019mixed,
  title={Mixed High-Order Attention Network for Person Re-Identification},
  author={Chen, Binghui and Deng, Weihong and Hu, Jiani},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year={2019},
}
@InProceedings{chen2019energy,
author = {Chen, Binghui and Deng, Weihong},
title = {Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019}
}

License

Copyright (c) Binghui Chen

All rights reserved.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.