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.
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 tested version are listed below:
- python: 3.6.7
- tensorflow: 1.12.0
- tensorflow-base: 1.12.0
- tensorflow-gpu: 1.12.0
- keras: 2.2.4
- keras-applications: 1.0.6
- keras-base: 2.2.4
- keras-preprocessing: 1.0.5
Before you run, you need to speicify the dataset directory in your local machine. 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 srcipt in the datasets folder. If you do so, set g_data_root = './datasets'.
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 then launch the terminal and run:
python reid.py
For the theory behind the code, please check with the wiki.