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code for CVPR2019 paper VPM (Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification)

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// repository documentation

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#### About

This repository contains the official code for paper [Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Perceive_Where_to_Focus_Learning_Visibility-Aware_Part-Level_Features_for_Partial_CVPR_2019_paper.pdf), CVPR 2019. If you find it helpful for your research, please kindly cite it as
```
@inproceedings{sun2019perceive,
  title={Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification},
  author={Sun, Yifan and Xu, Qin and Li, Yali and Zhang, Chi and Li, Yikang and Wang, Shengjin and Sun, Jian},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={393--402},
  year={2019}
}
```

#### Requirements

- Python 3
- Pytorch 1.0

#### Datasets

Make sure you have following dataset structure under the project directory
- datasets/market1501/Market-1501-v15.09.15
  - bounding_box_train
  - query
  - bounding_box_test
- datasets/partial_ilids/Partial_iLIDS
  - Probe
  - Gallery
- datasets/partial_reid/PartialREID
  - partial_body_images
  - whole_body_images

#### Train on Market1501

```bash
python PCB_tri_partial_column.py \
--gpu 0,1 \
-d market \
-a resnet50_pseudo_column \
-b 64 \
--log logs \
--data-dir datasets/market1501/Market-1501-v15.09.15 \
--step-size 80 \
--epochs 130 \
--optimizer sgd \
--lr 0.1 \
--dropout 0.5 \
--features 256
```

This script will also perform testing on Market1501 after training is done.

#### Test on Partial-iLIDS

```bash
python Partial_iLIDS_test.py \
-d partial_ilids \
-a resnet50_pseudo_column \
--logs-dir logs \
--data-dir datasets/partial_ilids/Partial_iLIDS \
--resume logs/checkpoint_130.pth.tar \
--dropout 0.5 \
--features 256
```

#### Test on Partial-REID

```bash
python Partial_REID_test.py \
-d partial_reid \
-a resnet50_pseudo_column \
--logs-dir logs \
--data-dir datasets/partial_reid/PartialREID \
--resume logs/checkpoint_130.pth.tar \
--dropout 0.5 \
--features 256
```

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