OpenISS Person Re-Identification Baseline

This repo basically is the OpenISS re-implementation (tensorflow + keras) of a person re-identification baseline proposed by the paper "Bag of Tricks and A Strong Baseline for Deep Person Re-identification".

The authors original implementation which is in Pytorch can be found in their repo.

This is a part of the Eric Lai's ML portion of the OpenISS project for his master's thesis:

• Haotao Lai, An OpenISS Framework Specialization for Person Re-identification, Master's thesis, August 2019, Concordia University, Montreal

See also: openiss-yolov3.

Environment

A powerful GPU is required for running the code, with Nivida GTX 1070ti, a training with the standard 120 epochs will take almost 4 hours.

This implementatoin is based on tensorflow and keras (currently not other backend are suppoted rather than tf).

The pre-requisites to prepare the environment are located in environment.yml.

We suggest you to run the script in any Python virtual environment and install the packages versions as described in environment.yml.

Run

Before you run the script, you need to specify the dataset directory location. Go to the reid.py file, check the global variable named g_data_root. If you don't have the dataset yet, you can get the dataset by using the script in the datasets folder. If you do so, set g_data_root = './datasets'.

To download the test dataset go to: datasets/ and run get_dataset_market1501.sh

To train or try the model out, go to the very end of the reid.py file. Comment the method you don't want and uncomment the method you want and run:

python reid.py

Results

The result files are located in output/cross_ds_v/

Theory

For the theory behind the code, please check with the wiki.

Configuration on Conda Virtual Environment

• Conda/anaconda must be installed in your system
• Clone this repo
• To download the dataset go to: datasets/ and run get_dataset_market1501.sh
• In reid.py set the epochs (g_epochs=120 by default)
• To create the environment: conda env create -f environment.yml -p /your_path/name_of_environment
• Run conda activate /your_path/name_of_environment
• Run python reid.py to execute the program
• once finished: conda deactivate
• To remove remove environment: conda env remove -p /your_path/name_of_environment