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.pyand runpython prepare.pyto 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.