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How to prepare data

Create a directory to store reid datasets under this repo via

cd deep-person-reid/
mkdir data/

If you wanna store datasets in another directory, you need to specify --root path_to_your/data when running the training code. Please follow the instructions below to prepare each dataset. After that, you can simply do -d the_dataset when running the training code.

Please do not call image dataset when running video reid scripts, otherwise error would occur, and vice versa.

Image ReID

Market1501 [7]:

  1. Download dataset to data/ from http://www.liangzheng.org/Project/project_reid.html.
  2. Extract dataset and rename to market1501. The data structure would look like:
market1501/
    bounding_box_test/
    bounding_box_train/
    ...
  1. Use -d market1501 when running the training code.

CUHK03 [13]:

  1. Create a folder named cuhk03/ under data/.
  2. Download dataset to data/cuhk03/ from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and extract cuhk03_release.zip, so you will have data/cuhk03/cuhk03_release.
  3. Download new split [14] 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 data/cuhk03. Finally, the data structure would look like
cuhk03/
    cuhk03_release/
    cuhk03_new_protocol_config_detected.mat
    cuhk03_new_protocol_config_labeled.mat
    ...
  1. Use -d cuhk03 when running the training code. In default mode, we use new split (767/700). If you wanna use the original splits (1367/100) created by [13], specify --cuhk03-classic-split. As [13] computes CMC differently from Market1501, you might need to specify --use-metric-cuhk03 for fair comparison with their method. In addition, we support both labeled and detected modes. The default mode loads detected images. Specify --cuhk03-labeled if you wanna train and test on labeled images.

DukeMTMC-reID [16, 17]:

  1. The process is automated, please use -d dukemtmcreid when running the training code. The final folder structure looks like as follows
dukemtmc-reid/
    DukeMTMC-reid.zip # (you can delete this zip file, it is ok)
    DukeMTMC-reid/

MSMT17 [22]:

  1. Create a directory named msmt17/ under data/.
  2. Download dataset MSMT17_V1.tar.gz to data/msmt17/ from http://www.pkuvmc.com/publications/msmt17.html. Extract the file under the same folder, so you will have
msmt17/
    MSMT17_V1.tar.gz # (do whatever you want with this .tar file)
    MSMT17_V1/
        train/
        test/
        list_train.txt
        ... (totally six .txt files)
  1. Use -d msmt17 when running the training code.

VIPeR [28]:

  1. The code supports automatic download and formatting. Just use -d viper as usual. The final data structure would look like:
viper/
    VIPeR/
    VIPeR.v1.0.zip # useless
    splits.json

GRID [29]:

  1. The code supports automatic download and formatting. Just use -d grid as usual. The final data structure would look like:
grid/
    underground_reid/
    underground_reid.zip # useless
    splits.json

CUHK01 [30]:

  1. Create cuhk01/ under data/ or your custom data dir.
  2. Download CUHK01.zip from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and place it in cuhk01/.
  3. Do -d cuhk01 to use the data.

PRID450S [31]:

  1. The code supports automatic download and formatting. Just use -d prid450s as usual. The final data structure would look like:
prid450s/
    cam_a/
    cam_b/
    readme.txt
    splits.json

SenseReID [32]:

  1. Create sensereid/ under data/ or your custom data dir.
  2. Download dataset from this link and extract to sensereid/. The final folder structure should look like
sensereid/
    SenseReID/
        test_probe/
        test_gallery/
  1. The command for using SenseReID is -d sensereid. Note that SenseReID is for test purpose only so training images are unavailable. Please use --evaluate along with -d sensereid.

Video ReID

MARS [8]:

  1. Create a directory named mars/ under data/.
  2. Download dataset to data/mars/ from http://www.liangzheng.com.cn/Project/project_mars.html.
  3. Extract bbox_train.zip and bbox_test.zip.
  4. Download split information from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put info/ in data/mars (we want to follow the standard split in [8]). The data structure would look like:
mars/
    bbox_test/
    bbox_train/
    info/
  1. Use -d mars when running the training code.

iLIDS-VID [11]:

  1. The code supports automatic download and formatting. Simple use -d ilidsvid when running the training code. The data structure would look like:
ilids-vid/
    i-LIDS-VID/
    train-test people splits/
    splits.json

PRID [12]:

  1. Under data/, do mkdir prid2011 to create a directory.
  2. Download dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under data/prid2011.
  3. Download the split created by iLIDS-VID from here, and put it in data/prid2011/. We follow [11] and use 178 persons whose sequences are more than a threshold so that results on this dataset can be fairly compared with other approaches. The data structure would look like:
prid2011/
    splits_prid2011.json
    prid_2011/
        multi_shot/
        single_shot/
        readme.txt
  1. Use -d prid2011 when running the training code.

DukeMTMC-VideoReID [16, 23]:

  1. Use -d dukemtmcvidreid directly.
  2. If you wanna download the dataset manually, get DukeMTMC-VideoReID.zip from https://github.com/Yu-Wu/DukeMTMC-VideoReID. Unzip the file to data/dukemtmc-vidreid. Ultimately, you need to have
dukemtmc-vidreid/
    DukeMTMC-VideoReID/
        train/ # essential
        query/ # essential
        gallery/ # essential
        ... (and license files)

Dataset loaders

These are implemented in dataset_loader.py where we have two main classes that subclass torch.utils.data.Dataset:

These two classes are used for torch.utils.data.DataLoader that can provide batched data. Data loader wich ImageDataset outputs batch data of (batch, channel, height, width), while data loader with VideoDataset outputs batch data of (batch, sequence, channel, height, width).

Evaluation

Image ReID

  • Market1501, DukeMTMC-reID, CUHK03 (767/700 split) and MSMT17 have fixed split so keeping split_id=0 is fine.
  • CUHK03 (classic split) has 20 fixed splits, so do split_id=0~19.
  • VIPeR contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits with split_id starting from 0 to 19. Models can be trained on split_id=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18] (because split_id=0 and split_id=1 share the same train set, etc.). At test time, models trained on split_id=0 can be directly evaluated on split_id=1, models trained on split_id=2 can be directly evaluated on split_id=3, and so on and so forth.
  • CUHK01 is similar to VIPeR in split generation so evaluation should be done for split_id=0~19.
  • GRID and PRID450S has 10 random splits, so evaluation is done by varying split_id from 0 to 9.
  • SenseReID has no training images and is used for evaluation only, therefore, --evaluate must be used.

Video ReID

  • MARS and DukeMTMC-VideoReID have fixed single split so using -d dataset_name and split_id=0 is ok.
  • iLIDS-VID and PRID2011 have 10 predefined splits so evaluation can be done by varying split_id from 0 to 9.