/Pytorch-DukeMTMC-Baseline

A Pytorch based Imlemention of DukeMTMC-reID baseline(Resnet18,Resnet34 and Resnet50)

Primary LanguagePython

A Pytorch based Implementation of DukeMTMC-reID baseline(Resnet18,Resnet34 and Resnet50)

Introduction:

This project provides pytorch based implementation of Resnet18,Resnet34 and Resnet50. Their performance on DukeMTMC-reID are listed below (20 epochs):

Model Name Rank 1 Acc mAP
Resnet 18       69.39%       0.4590
Resnet 34       74.10%       0.4860
Resnet 50       72.94%       0.4812  

Requirements:

fire,pytorch,torchvision,numpy are required to run this demo.

Pytorch torchvision

see http://pytorch.org/ for more information, you are supposed to install pytorch 0.3

fire

pip install fire

numpy

pip install numpy

tqdm

pip install tqdm

Usage:

1. Train Model

create 3 directories:dataReader/readyTrain dataReader/test and dataReader/query and then execute download.sh to download images

change line 29 of config.py to select the model you want to train,the following models are supported:

  • Resnet 50 ---- change self.model's value to "resnet50"
  • Resnet 18 ---- change self.model's value to "resnet18"
  • Resnet 34 ---- change self.model's value to "resnet34"

open a terminal in project's folder and run

python reid.py train --modelPath=None

You can also change the model's training parameters with '--' in terminal, for example, if you want to change the initial learning rate of model to 0.01, you can run this command to start training:

python reid.py train --modelPath=None --lr=0.01

During the training process, you will get your model's weight file called ($modelname+$time).pth in 'snapshots' folder (please create it) in every 10 epoch. You can change the frequency in line 31 of config.py(self.snapFreq)

warning: if you want to train the model with target pth file, you may change the modelPath value in config.py and run the command above without '--modelPath=None'.

2. Get Features(Test)

After training, run this command to get allF.pth, which stores all features of images in 'dataReader\test'

python reid.py test

3. Query your Image

If you want to query target image ,just check the serial number of the image(for example, 0005_c2_f0046985.jpg is the first image in 'dataReader\query' so its serial number is 0) in query image folder and run this command in terminal

python reid.py query 0

If you run command like this

python reid.py query

all images in 'dataReader\query' will be queried and Rank1-6 values will be listed afterwards.

Reference and Acknowledgment:

I referenced the code on https://github.com/chenyuntc/pytorch-best-practice and I think it's the best tutorial code for pytorch beginners

Thank for the guidance from zhunzhong07 and layumi

Log

  • 2018.5.2

    1. update pytorch to 0.4.0, in 0.4.0, old code may have error. To avoid this, new version is now available in debug branch.
    2. tqdm is added to show a progress bar dynamicly.
    3. remove visdom support, there is no need to use visdom. I shall add it when necessary.

  • 2018.5.3

    1. Code has been tested on market-1501. Results are listed below:
Model Name Rank 1 Acc mAP
Resnet 18       81.00%       0.5042
Resnet 34       -       -
Resnet 50       86.49%       0.5536  
  1. Give up pytorch 0.4.0 support.
  • 2018.5.7

 1. Some reports that mAP calculation of the code is not correct, we shall check and revise all eval part of the project afterwards.