Awesome

• Multi-shot Re-identification
• Preparations
• Usage

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

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

• Download the MARS , iLIDS-VID and PRID-2011.
• Install MXNet following the instructions and install the python interface. Currently the repo is tested on commit e06c55.

• 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.