haoni0812 / MDA

Meta Distribution Alignment for Generalizable Person Re-Identification (MDA)

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1) Prerequisites

• Ubuntu 18.04
• Python 3.6
• Pytorch 1.7+
• NVIDIA GPU (>=8,000MiB)
• Anaconda 4.8.3
• CUDA 10.1 (optional)
• Recent GPU driver (Need to support AMP [link])

2) Datasets

• (1) Market1501

  • Create a directory named Market-1501-v15.09.15
  • Download the dataset to Market-1501-v15.09.15 from link and extract the files.
  • The data structure should look like

  Market-1501-v15.09.15/
  ├── bounding_box_test/
  ├── bounding_box_train/
  ├── gt_bbox/
  ├── gt_query/
  ├── query/
  

• (2) DukeMTMC-reID

  • Create a directory called DukeMTMC-reID
  • Download DukeMTMC-reID from link and extract the files.
  • The data structure should look like

  DukeMTMC-reID/
  ├── bounding_box_test/
  ├── bounding_box_train/
  ├── query/
  

• (3) CUHK02

  • Create cuhk02 folder
  • Download the data from link and put it under cuhk02.
    • The data structure should look like

  cuhk02/
  ├── P1/
  ├── P2/
  ├── P3/
  ├── P4/
  ├── P5/
  

• (4) CUHK03

  • Create cuhk03 folder
  • Download dataset to cuhk03 from link and extract “cuhk03_release.zip”, resulting in “cuhk03/cuhk03_release/”.
  • Download the new split (767/700) from person-re-ranking. What you need are “cuhk03_new_protocol_config_detected.mat” and “cuhk03_new_protocol_config_labeled.mat”. Put these two mat files under cuhk03.
  • The data structure should look like

  cuhk03/
  ├── cuhk03_release/
  ├── cuhk03_new_protocol_config_detected.mat
  ├── cuhk03_new_protocol_config_labeled.mat
  

• (5) Person Search (CUHK-SYSU)

  • Create a directory called CUHK-SYSU
  • Download CUHK-SYSU from link and extract the files.
  • Cropped images can be created by my matlab code make_cropped_image.m (this code is included in the datasets folder)
  • The data structure should look like

  CUHK-SYSU/
  ├── annotation/
  ├── Image/
  ├── cropped_image/
  ├── make_cropped_image.m (my matlab code)
  

• (6) GRID

  • Create a directory called GRID
  • Download GRID from link and extract the files.
  • Split sets (splits.json) can be created by python code grid.py
  • The data structure should look like

  GRID/
  ├── gallery/
  ├── probe/
  ├── splits_single_shot.json (This will be created by `grid.py` in `fastreid/data/datasets/` folder)
  

• (7) PRID

  • Create a directory called prid_2011
  • Download prid_2011 from link and extract the files.
  • Split sets (splits_single_shot.json) can be created by python code prid.py
  • The data structure should look like

  prid_2011/
  ├── single_shot/
  ├── multi_shot/
  ├── splits_single_shot.json (This will be created by `prid.py` in `fastreid/data/datasets/` folder)
  

• (8) QMUL i-LIDS

  • http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz
  • https://github.com/BJTUJia/person_reID_DualNorm
  • Create a directory called QMUL_iLIDS
  • Download QMUL_iLIDS from the upper links
  • Split sets can be created by python code iLIDS.py
  • The data structure should look like

  QMUL-iLIDS/
  ├── images/
  ├── splits.json (This will be created by `iLIDS.py` in `fastreid/data/datasets/` folder)
  

• (9) VIPer

  • Create a directory called viper
  • Download viper from link and extract the files.
  • Split sets can be created by my matlab code make_split.m (this code is included in the datasets folder)
  • The data structure should look like

  viper/
  ├── cam_a/
  ├── cam_b/
  ├── make_split.m (my matlab code)
  ├── split_1a # Train: split1, Test: split2 ([query]cam1->[gallery]cam2)
  ├── split_1b # Train: split2, Test: split1 (cam1->cam2)
  ├── split_1c # Train: split1, Test: split2 (cam2->cam1)
  ├── split_1d # Train: split2, Test: split1 (cam2->cam1)
  ...
  ...
  ├── split_10a
  ├── split_10b
  ├── split_10c
  ├── split_10d
  

2) Train

python3 ./tools/train_net.py --config-file ./configs/Sample/DG-mobilenet.yml

• Evaluation only

python3 ./tools/train_net.py --config-file ./configs/Sample/DG-mobilenet.yml --eval-only