# DCR

**Repository Path**: Lishuai_Gao/dcr

## Basic Information

- **Project Name**: DCR
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-02
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Partial-Person-ReID
******************************************************************************************************************

**The source code**: Spatial Feature Reconstruction with Pyramid Pooling for Partial Person Re-identification 
CVPR18: [Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach](http://openaccess.thecvf.com/content_cvpr_2018/papers/He_Deep_Spatial_Feature_CVPR_2018_paper.pdf), Arxiv18 

The project provides the training and testing code for partial person re-id, using [Pytorch](https://pytorch.org/)

## Instllation
*****************************************************************************************************************
It's recommended that you create and enter a python virtual environment, if versions of the packages required here conflict with yours.

Other packages are specified in `requirements.txt`

## Daset Preparation
Inspired by Houjing Huang's [person-reid-triplet-loss-baseline](https://github.com/huanghoujing/person-reid-triplet-loss-baseline) project, you can follow his guidance.

## Experiment Setting:
1. Backbone: ResNet-50, `stride = 1` in the last conv block.
2. Input image size: `384 & times 192`

## Person Re-identification
### Result on Market1501
```
python script/experiment/train.py \
--dataset market1501 \
--partial_dataset others\
--Spatial_train False \
--total_epochs 400 
```
   | Method | Rank-1 (Single query) | mAP | Rank-1 (Multi query)| mAP |
| - | :-: | -: |  :-: | -: | 
| Baseline | 88.18| 73.85 | 92.25 | 80.96|
| SFR | 93.04 | 81.02 | 94.84 | 85.47 |

### Result on CUHK03
```
python script/experiment/train.py \
--dataset cuhk03 \
--partial_dataset others\
--Spatial_train False \
--total_epochs 400 
```
   | Method | Rank-1 (Labeled) | mAP | Rank-1 (Detected)| mAP |
| - | :-: | -: |  :-: | -: | 
| Baseline | 62.14| 58.47 | 60.43 | 54.24|
| SFR | 67.29 |61.47 | 63.86 | 58.97 |

### Result on Duke
```
python script/experiment/train.py \
--dataset duke \
--partial_dataset others\
--Spatial_train False \
--total_epochs 400 
```
   | Method | Rank-1 (Labeled) | mAP|
| - | :-: | -: | 
| Baseline | 80.48| 64.80 |
| SFR | 84.83 |71.24 | 

## Partial Person Re-identification
The link of Partial REID and Partial iLIDS datasets: [Baidu Cloud](https://pan.baidu.com/s/1RWaGahSDO_bs6eWexBIxuw).

Before run the code, you should revise the path in `Partial_REID_test.py` and `Partial_iLIDS_test.py` to your path.
### Result on Partial REID

```
python script/experiment/train.py \
--dataset market1501 \
--partial_dataset Partial_REID\
--Spatial_train False \
--total_epochs 400 
```
   | Method | Rank-1  | Rank-5 |
| - | :-: | -: | 
| Baseline | 54.80| 80.20 | 
| SFR | 66.20 | 86.67 |

### Result on Partial iLIDS
```
python script/experiment/train.py \
--dataset market1501 \
--partial_dataset Partial_iLIDS\
--Spatial_train False \
--total_epochs 400 
```
   | Method | Rank-1  | Rank-5 |
| - | :-: | -: | 
| Baseline | 46.22| 74.79 | 
| SFR | 63.87 | 86.55 |


if you want to add the spatial feature reconstruction (SFR) in training term, please set `Spatial_train=True`, but it would increase the training time.

# Citing Spatial Feature Reconstruction

If you find SFR is useful in your research, pls consider citing:
```
@InProceedings{He_2018_CVPR,
author = {He, Lingxiao and Liang, Jian and Li, Haiqing and Sun, Zhenan}, 
title = {Deep Spatial Feature Reconstruction for Partial Person Re-Identification: Alignment-Free Approach},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018}
} ```