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6 |
7 | 8 | Paper PDF 9 | 10 |

11 | 12 | 13 | ## Getting Started 14 | * Clone this repo: 15 | 16 | `git clone https://github.com/bitreidgroup/VI-ReID-MSCLNet.git` 17 | 18 | `cd VI-ReID-MSCLNet` 19 | 20 | * Create a conda environment and activate the environment 21 | `conda env create -f environment.yml` 22 | 23 | `conda activate mscl` 24 | 25 | *We recommend Python = 3.6, CUDA = 10.0, Cudnn = 7.6.5, Pytorch = 1.2, and CudaToolkit = 10.0.130 for the environment.* 26 | 27 | ## Preparing dataset 28 | 29 | - **RegDB Dataset** : The RegDB dataset can be downloaded from this [website](http://dm.dongguk.edu/link.html) by submitting a copyright form. 30 | 31 | (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website). 32 | 33 | *We do not preprocess the RegDB dataset.* 34 | 35 | - **SYSU-MM01 Dataset** : The SYSU-MM01 dataset can be downloaded from this [website](http://isee.sysu.edu.cn/project/RGBIRReID.htm). 36 | 37 | - We preprocess the SYSU-MM01 dataset to speed up the training process. 38 | 39 | - if you do not need the identities of the cameras, run the [preprocess scripts](https://github.com/mangye16/Cross-Modal-Re-ID-baseline/blob/master/pre_process_sysu.py) 40 | 41 | `python pre_process_sysu.py` 42 | 43 | After running, the training data will be stored in ".npy" format. 44 | 45 | - if your need the identities of the cameras, run : 46 | 47 | `python pre_process_sysu_cam.py` 48 | 49 | The identities of cameras will be also stored in ".npy" format. 50 | 51 | ### Pre-trained Models and Reproduce our experimental results 52 | 53 | *You may need manually define the data path in the `utils/data_loader.py` and `utils/data_manager.py` first.* 54 | 55 | ```bash 56 | bash scripts/reproduce.sh 57 | ``` 58 | 59 | ### 4. Citation 60 | 61 | If this repository helps your research, please cite : 62 | 63 | ``` 64 | @inproceedings{zhang2022modality, 65 | title={Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification}, 66 | author={Zhang, Yiyuan and Zhao, Sanyuan and Kang, Yuhao and Shen, Jianbing}, 67 | booktitle={European Conference on Computer Vision}, 68 | pages={462--479}, 69 | year={2022}, 70 | organization={Springer} 71 | } 72 | ``` 73 | 74 | ## Acknowledgement 75 | Many thanks to the authors of [AGW](https://github.com/mangye16/Cross-Modal-Re-ID-baseline) 76 | -------------------------------------------------------------------------------- /asset/MSCLNet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bitreidgroup/VI-ReID-MSCLNet/8c94080703409ee3a7ec9be04aa919b7704ee676/asset/MSCLNet.pdf -------------------------------------------------------------------------------- /asset/pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bitreidgroup/VI-ReID-MSCLNet/8c94080703409ee3a7ec9be04aa919b7704ee676/asset/pipeline.png -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: mscl 2 | channels: 3 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main 4 | - https://mirrors.ustc.edu.cn/anaconda/pkgs/main 5 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ 6 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ 7 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ 8 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ 9 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/menpo/ 10 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ 11 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ 12 | - https://mirrors.ustc.edu.cn/anaconda/pkgs/free/ 13 | - https://mirrors.ustc.edu.cn/anaconda/pkgs/main/ 14 | - defaults 15 | dependencies: 16 | - certifi=2021.5.30=py36h06a4308_0 17 | - cffi=1.14.0=py36h2e261b9_0 18 | - intel-openmp=2021.4.0=h06a4308_3561 19 | - lcms2=2.12=h3be6417_0 20 | - libedit=3.1=heed3624_0 21 | - libffi=3.2.1=hf484d3e_1007 22 | - libgcc-ng=9.1.0=hdf63c60_0 23 | - libstdcxx-ng=9.1.0=hdf63c60_0 24 | - libtiff=4.2.0=h85742a9_0 25 | - lz4-c=1.9.3=h295c915_1 26 | - mkl=2020.2=256 27 | - mkl_random=1.1.1=py36h0573a6f_0 28 | - numpy=1.19.2=py36h54aff64_0 29 | - numpy-base=1.19.2=py36hfa32c7d_0 30 | - openjpeg=2.4.0=h3ad879b_0 31 | - openssl=1.0.2u=h7b6447c_0 32 | - pillow=8.3.1=py36h2c7a002_0 33 | - pycparser=2.20=py_2 34 | - pytorch=1.2.0=cuda100py36h938c94c_0 35 | - readline=7.0=ha6073c6_4 36 | - setuptools=58.0.4=py36h06a4308_0 37 | - six=1.16.0=pyhd3eb1b0_0 38 | - wheel=0.37.0=pyhd3eb1b0_1 39 | - zstd=1.4.9=haebb681_0 40 | - _libgcc_mutex=0.1=main 41 | - _pytorch_select=0.2=gpu_0 42 | - blas=1.0=mkl 43 | - ca-certificates=2021.10.26=h06a4308_2 44 | - cudatoolkit=10.0.130=0 45 | - cudnn=7.6.5=cuda10.0_0 46 | - freetype=2.11.0=h70c0345_0 47 | - jpeg=9d=h7f8727e_0 48 | - libpng=1.6.37=hbc83047_0 49 | - libwebp-base=1.2.0=h27cfd23_0 50 | - mkl-service=2.3.0=py36he8ac12f_0 51 | - mkl_fft=1.3.0=py36h54f3939_0 52 | - ncurses=6.0=h9df7e31_2 53 | - ninja=1.10.2=hff7bd54_1 54 | - olefile=0.46=pyhd3eb1b0_0 55 | - pip=21.2.2=py36h06a4308_0 56 | - python=3.6.2=hca45abc_19 57 | - sqlite=3.23.1=he433501_0 58 | - tk=8.6.11=h1ccaba5_0 59 | - torchvision=0.4.0=cuda100py36hecfc37a_0 60 | - xz=5.2.5=h7b6447c_0 61 | - zlib=1.2.11=h7b6447c_3 62 | - pip: 63 | - backcall==0.2.0 64 | - click==7.0 65 | - cycler==0.10.0 66 | - decorator==5.1.0 67 | - entrypoints==0.3 68 | - faiss-gpu==1.7.1.post3 69 | - flask==1.0.3 70 | - flask-cors==3.0.7 71 | - infomap==1.9.0 72 | - ipykernel==5.5.6 73 | - ipython==7.16.2 74 | - ipython-genutils==0.2.0 75 | - itsdangerous==1.1.0 76 | - jedi==0.17.2 77 | - jinja2==2.10.1 78 | - joblib==1.1.0 79 | - jupyter-client==7.1.0 80 | - jupyter-core==4.9.1 81 | - kiwisolver==1.1.0 82 | - markupsafe==1.1.1 83 | - matplotlib==3.3.4 84 | - nest-asyncio==1.5.4 85 | - pandas==1.1.5 86 | - parso==0.7.1 87 | - pexpect==4.8.0 88 | - pickleshare==0.7.5 89 | - prompt-toolkit==3.0.23 90 | - protobuf==3.19.1 91 | - ptyprocess==0.7.0 92 | - pygments==2.10.0 93 | - pyparsing==2.4.0 94 | - python-dateutil==2.8.0 95 | - pytz==2021.3 96 | - pyzmq==22.3.0 97 | - scikit-learn==0.24.2 98 | - scipy==1.5.4 99 | - seaborn==0.11.2 100 | - tensorboardx==2.1 101 | - threadpoolctl==3.0.0 102 | - torch==1.2.0 103 | - tornado==6.1 104 | - tqdm==4.62.3 105 | - traitlets==4.3.3 106 | - wcwidth==0.2.5 107 | - werkzeug==0.15.4 108 | prefix: /home/usr/.conda/envs/mscl 109 | 110 | --------------------------------------------------------------------------------