Skip to content

Beyond Part Models: Person Retrieval with Refined Part Pooling

Notifications You must be signed in to change notification settings

decajcd/ReID-PCB_RPP

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ReID-PCB_RPP

This repository tries to implement the paper:Beyond Part Models: Person Retrieval with Refined Part Pooling and I almost follow the training details at the origin paper.

Market-1501 Results

  • The result of PCB is little higher than paper.
  • The result of PCB+RPP is little lower than paper.
  • I think if we do more augmentation with the training data, the performance will be better.
Feature mAP(%) Rank-1(%)
PCB(paper) G 77.4 92.3
PCB(paper) H 77.3 92.4
PCB(ours) G 78.6 92.7
PCB(ours) H 78.5 92.1
PCB+RPP(paper) G 81.6 93.8
PCB+RPP(paper) H 81.0 93.1
PCB+RPP(ours) G 80.7 92.8
PCB+RPP(ours) H 79.8 92.4

Prerequisites

  • Python 2.7 or 3.5
  • Pytorch 0.4
  • GPU Memory

Getting started

Dataset

  • Dwonload Market1501 Dataset and extract the files to the current folder
  • Change the download_path in the prepare.py and run python prepare.py to prepare the dataset

Train

Train the model:

python train.py --gpu_ids 0,1 --batchsize 64 --data_dir your_data_path --save_dir your_model_save_path --RPP True
  • --gpu_ids: single GPU or mutil-GPUs
  • --batchsize: batch size
  • --data_dir: the path of the preparing data
  • --save_dir: the path to save the training model
  • --RPP: whether to train with RPP I train the model on the two GPUs with 64 batchsize

Test

Test the model(extract the features):

python test.py --gpu_ids 0 --which_epoch select the model --stage PCB or full --RPP True or False --feature_H True or False
  • --gpu_ids: just single GPU
  • --which_epoch: select the i-th model
  • --stage: select the training model : PCB or full(PCB+RPP)
  • --RPP: if you choose the PCB model, RPP to False; if you choose the full model, RPP to True
  • --feature_H: whether to extract the low-dims features

Evaluate

Evaluate the model

  • Evaluate on GPU:
python evaluate_gpu.py --gpu_ids 0 --reslut_mat the path of  the features mat
  • Evaluate on CPU:
python evaluate_gpu.py --reslut_mat the path of  the features mat

Reference resources

Thanks to the layumi/Person_reID_baseline_pytorch. This repository only implements the part of PCB, I make some modifications on it and then add the RPP part.

About

Beyond Part Models: Person Retrieval with Refined Part Pooling

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%