xiaoccer / reid-pcb_rpp

Beyond Part Models: Person Retrieval with Refined Part Pooling

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.