Skip to content

Latest commit

 

History

History
68 lines (57 loc) · 3.15 KB

DATASETS.md

File metadata and controls

68 lines (57 loc) · 3.15 KB
Raw

How to prepare data

Create a directory to store reid datasets under this repo via

cd AGRL.pytorch/
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.

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 with VideoDataset outputs batch data of (batch, sequence, channel, height, width).