Skip to content

Multi-shot Pedestrian Re-identification via Sequential Decision Making (CVPR2018)

Notifications You must be signed in to change notification settings

TuSimple/rl-multishot-reid

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 

Repository files navigation

Multi-shot Re-identification Based on Reinforcement Learning


Training and testing codes for multi-shot Re-Identification. Currently, these codes are tested on the PRID-2011 dataset, iLiDS-VID dataset and MARS dataset. For algorithm details and experiment results, please refer our paper: Multi-shot Pedestrian Re-identification via Sequential Decision Making

Preparations


Before starting running this code, you should make the following preparations:

Usage


  • Download the datasets and unzip.
  • Prepare data file. Generate image list file according to the file preprocess_ilds_image.py , preprocess_prid_image.py and preprocess_mars_image.py under baseline folder.
  • The code is split to two stage, the first stage is a image based re-id task, please refer the script run.sh in baseline folder. The codes for this stage is based on this repo. The usage is:
sh run.sh $gpu $dataset $network $recfloder

e.g. If you want to train MARS dataset on gpu 0 using inception-bn, please run:

sh run.sh 0 MARS inception-bn /data3/matt/MARS/recs
  • The second stage is a multi-shot re-id task based on reinforcement learning. Please refer the script run.sh in RL folder. The usage is:
sh run.sh $gpu $unsure-penalty $dataset $network $recfloder
  • For evaluation, please use baseline/baseline_test.py and RL/find_eg.py. In RL/find_eg.py, we also show some example episodes with good quality generated by our algorithm.

About

Multi-shot Pedestrian Re-identification via Sequential Decision Making (CVPR2018)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published