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ecn's Introduction

Preparation

Requirements: Python=3.6, Pytorch>=1.0.0

  1. Install Pytorch and clone this repository or download this repositor

  2. Download dataset (Manually perform this! If you prefer automatic please follow 2a)

    Ensure the File structure is as follow:

    ECN/data    
    │
    └───market OR duke
       │   
       └───bounding_box_train
       │   
       └───bounding_box_test
       │   
       └───bounding_box_train_camstyle_cyclegan
       │ 
       └───query
    

2a. Download dataset (Automatically perform by running the script)

  • cd into 'data' folder

  • Run 'bash make_duke.sh', 'bash make_market.sh' and 'bash make_stargan.sh'

Ensure the File structure is as follow:

ECN/data    
│
└───market OR duke
   │   
   └───bounding_box_train
   │   
   └───bounding_box_test
   │   
   └───bounding_box_train_camstyle_cyclegan
   │   
   └───bounding_box_train_camstyle_stargan
   │ 
   └───query

Training and test domain adaptation model for person re-ID

This will produce all the results that are shown in the paper. This generates a log file for every run and the last 5 lines of the log file contains the mAP scores and other results.

# python3 main.py -s duke -t market -cs cyclegan -mmd 1 --lmd 0.3 --lmd_ext 0.33
# python3 main.py -s duke -t market -cs cyclegan -mmd 1 --lmd 0.3 --lmd_ext 0.33  --evaluate --resume logs/checkpoint.pth.tar

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manasakay avatar shreenibhar avatar ssrivaishnavi avatar zhunzhong07 avatar

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