The pytorch-based lightweight library of person re-identification.
Version
python 3.6 or 3.7
pytorch >= 0.4
Install
pip install numpy h5py lmdb
pip install visdom # Optional. If you don't need a web page visualization, don't install it.
Install pytorch and torchvision
Indicates the folder of the original files and where the unzipped file is placed.
# person-reid-lib/lib/utils/manager.py
self._device_dict = xxxx
Install opencv
pip install opencv-contrib-python # version 3.4.2.17
Config
# person-reid-lib/lib/dataset/utils.py
DataStoreManager.store_optical_flow = True # if you want to use optical flow, enable it.
# person-reid-lib/tasks/taskname/solver.py
Solver.use_flow = True
# image-dataset
cd person-reid-lib_folder
sh script/server_0.sh
# video-dataset
cd person-reid-lib_folder
sh script/task_video.sh
Image: VIPeR, Market1501, CUHK03, CUHK01, DukeMTMCreID, GRID,
Video : iLIDS-VID, PRID-2011, LPW, MARS, DukeMTMC-VideoReID
2018.12.29 The code of Spatial and Temporal Mutual Promotion for Video-based Person Re-identification is available.
2018.12.26 The initial version is available.
2018.11.19 The code for lib has been released.