CVPR2020 Pose-guided Visible Part Matching for Occluded Person ReID

This is the pytorch implementation on windows of the CVPR2020 paper "Pose-guided Visible Part Matching for Occluded Person ReID"

Contact

Email: nickhuang1996@126.com

Modification

I fixed the way to load the model weights, or you will have severe errors when model weights are loaded:
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbc in position 0: invalid start byte
I fixed the rank_cy.pyx and compiled successfully, so you just make this repository and test models.
I fixed the setup.py so you can use cython to compile:
with open('README.md', encoding='latin1') as f:

Supplementary Specification

Compile

You need to download Visual Studio 2017 or Visual Studio 2019 to compile this repository.
Run python set.up develop. If you compile successfully, the cmd is:

D:\project\Pycharm\PVPM-master>python setup.py develop
D:\project\Pycharm\PVPM-master\torchreid\metrics\rank.py:17: UserWarning: Cython evaluation (very fast so highly recommended) is unavailable, now use python evaluation.
  'Cython evaluation (very fast so highly recommended) is '
Compiling torchreid/metrics/rank_cylib/rank_cy.pyx because it changed.
[1/1] Cythonizing torchreid/metrics/rank_cylib/rank_cy.pyx
D:\software\Anaconda3\lib\site-packages\Cython\Compiler\Main.py:367: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: D:\project\Pycharm\PVPM-master\torchreid\metrics\rank_cylib\rank_cy.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
running develop
running egg_info
creating torchreid.egg-info
writing torchreid.egg-info\PKG-INFO
writing dependency_links to torchreid.egg-info\dependency_links.txt
writing requirements to torchreid.egg-info\requires.txt
writing top-level names to torchreid.egg-info\top_level.txt
writing manifest file 'torchreid.egg-info\SOURCES.txt'
reading manifest file 'torchreid.egg-info\SOURCES.txt'
writing manifest file 'torchreid.egg-info\SOURCES.txt'
running build_ext
building 'torchreid.metrics.rank_cylib.rank_cy' extension
creating build
creating build\temp.win-amd64-3.7
creating build\temp.win-amd64-3.7\Release
creating build\temp.win-amd64-3.7\Release\torchreid
creating build\temp.win-amd64-3.7\Release\torchreid\metrics
creating build\temp.win-amd64-3.7\Release\torchreid\metrics\rank_cylib
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MT -ID:\software\Anaconda3\lib\site-packages\numpy\core\include -ID:\software\Anaconda3\include -ID:\software\Anaconda3\include "-IC:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\ATLMFC\include" "-IC:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\include" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\winrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\cppwinrt" /Tctorchreid/metrics/rank_cylib/rank_cy.c /Fobuild\temp.win-amd64-3.7\Release\torchreid/metrics/rank_cylib/rank_cy.obj
rank_cy.c
D:\software\Anaconda3\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
torchreid/metrics/rank_cylib/rank_cy.c(3810): warning C4244: “=”: 从“__int64”转换到“float”，可能丢失数据
torchreid/metrics/rank_cylib/rank_cy.c(4523): warning C4244: “=”: 从“double”转换到“float”，可能丢失数据
torchreid/metrics/rank_cylib/rank_cy.c(4553): warning C4244: “=”: 从“double”转换到“float”，可能丢失数据
torchreid/metrics/rank_cylib/rank_cy.c(5645): warning C4244: “=”: 从“__int64”转换到“float”，可能丢失数据
torchreid/metrics/rank_cylib/rank_cy.c(5808): warning C4244: “=”: 从“double”转换到“float”，可能丢失数据
torchreid/metrics/rank_cylib/rank_cy.c(5858): warning C4244: “=”: 从“double”转换到“float”，可能丢失数据
creating D:\project\Pycharm\PVPM-master\build\lib.win-amd64-3.7
creating D:\project\Pycharm\PVPM-master\build\lib.win-amd64-3.7\torchreid
creating D:\project\Pycharm\PVPM-master\build\lib.win-amd64-3.7\torchreid\metrics
creating D:\project\Pycharm\PVPM-master\build\lib.win-amd64-3.7\torchreid\metrics\rank_cylib
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX86\x64\link.exe /nologo /INCREMENTAL:NO /LTCG /nodefaultlib:libucrt.lib ucrt.lib /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:D:\software\Anaconda3\libs /LIBPATH:D:\software\Anaconda3\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\ATLMFC\lib\x64" "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\lib\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.18362.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.18362.0\um\x64" /EXPORT:PyInit_rank_cy build\temp.win-amd64-3.7\Release\torchreid/metrics/rank_cylib/rank_cy.obj /OUT:build\lib.win-amd64-3.7\torchreid\metrics\rank_cylib\rank_cy.cp37-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.7\Release\torchreid/metrics/rank_cylib\rank_cy.cp37-win_amd64.lib
  正在创建库 build\temp.win-amd64-3.7\Release\torchreid/metrics/rank_cylib\rank_cy.cp37-win_amd64.lib 和对象 build\temp.win-amd64-3.7\Release\torchreid/metrics/rank_cylib\rank_cy.cp37-win_amd64.exp
正在生成代码
已完成代码的生成
copying build\lib.win-amd64-3.7\torchreid\metrics\rank_cylib\rank_cy.cp37-win_amd64.pyd -> torchreid\metrics\rank_cylib
Creating d:\software\anaconda3\lib\site-packages\torchreid.egg-link (link to .)
torchreid 0.8.1 is already the active version in easy-install.pth

Installed d:\project\pycharm\pvpm-master
Processing dependencies for torchreid==0.8.1
Searching for torchvision==0.4.2
Best match: torchvision 0.4.2
Adding torchvision 0.4.2 to easy-install.pth file

Using d:\software\anaconda3\lib\site-packages
Searching for torch==1.4.0
Best match: torch 1.4.0
Adding torch 1.4.0 to easy-install.pth file
Installing convert-caffe2-to-onnx-script.py script to D:\software\Anaconda3\Scripts
Installing convert-caffe2-to-onnx.exe script to D:\software\Anaconda3\Scripts
Installing convert-onnx-to-caffe2-script.py script to D:\software\Anaconda3\Scripts
Installing convert-onnx-to-caffe2.exe script to D:\software\Anaconda3\Scripts

Using d:\software\anaconda3\lib\site-packages
Searching for scipy==1.2.1
Best match: scipy 1.2.1
Adding scipy 1.2.1 to easy-install.pth file

Using d:\software\anaconda3\lib\site-packages
Searching for six==1.12.0
Best match: six 1.12.0
Adding six 1.12.0 to easy-install.pth file

Using d:\software\anaconda3\lib\site-packages
Searching for Pillow==5.4.1
Best match: Pillow 5.4.1
Adding Pillow 5.4.1 to easy-install.pth file

Using d:\software\anaconda3\lib\site-packages
Searching for h5py==2.9.0
Best match: h5py 2.9.0
Adding h5py 2.9.0 to easy-install.pth file

Using d:\software\anaconda3\lib\site-packages
Searching for Cython==0.29.6
Best match: Cython 0.29.6
Adding Cython 0.29.6 to easy-install.pth file
Installing cygdb-script.py script to D:\software\Anaconda3\Scripts
Installing cygdb.exe script to D:\software\Anaconda3\Scripts
Installing cython-script.py script to D:\software\Anaconda3\Scripts
Installing cython.exe script to D:\software\Anaconda3\Scripts
Installing cythonize-script.py script to D:\software\Anaconda3\Scripts
Installing cythonize.exe script to D:\software\Anaconda3\Scripts

Using d:\software\anaconda3\lib\site-packages
Searching for numpy==1.16.2
Best match: numpy 1.16.2
Adding numpy 1.16.2 to easy-install.pth file
Installing f2py-script.py script to D:\software\Anaconda3\Scripts
Installing f2py.exe script to D:\software\Anaconda3\Scripts

Using d:\software\anaconda3\lib\site-packages
Finished processing dependencies for torchreid==0.8.1

Dependencies

-Python2.7 or Python>=3.6
-Pytorch>=1.0
-Numpy

Related Project

Our code is based on deep-person-reid. We adopt openpose to extract pose landmarks and part affinity fields.

Dataset Preparation

Download the raw datasets Occluded-REID, P-DukeMTMC-reID, and Partial-Reid (code:zdl8) which is released by Partial Person Re-identification. Instructions regarding how to prepare Market1501 datasets can be found here. And then place them under the directory like:

PVPM_experiments/data/
├── ICME2018_Occluded-Person-Reidentification_datasets
│   ├── Occluded_Duke
│   └── Occluded_REID
├── Market-1501-v15.09.15
└── Partial-REID_Dataset

Pose extraction

Install openopse as described here.
Change path to your own dataset root and run sh files in /scripts:

sh openpose_occluded_reid.sh
sh openpose_market.sh

Extracted Pose information can be found here(code:iwlz)

To Train PCB baseline

python scripts/main.py --root PATH_TO_DATAROOT \
 -s market1501 -t market1501\
 --save-dir PATH_TO_EXPERIMENT_FOLDER/market_PCB\
 -a pcb_p6 --gpu-devices 0 --fixbase-epoch 0\
 --open-layers classifier fc\
 --new-layers classifier em\
 --transforms random_flip\
 --optim sgd --lr 0.02\
 --stepsize 25 50\
 --staged-lr --height 384 --width 192\
 --batch-size 32 --base-lr-mult 0.5

To train PVPM

python scripts/main.py --load-pose --root PATH_TO_DATAROOT
 -s market1501\
 -t occlusion_reid p_duke partial_reid\
 --save-dir PATH_TO_EXPERIMENT_FOLDER/PVPM\
 -a pose_p6s --gpu-devices 0\
 --fixbase-epoch 30\
 --open-layers pose_subnet\
 --new-layers pose_subnet\
 --transforms random_flip\
 --optim sgd --lr 0.02\
 --stepsize 15 25 --staged-lr\
 --height 384 --width 128\
 --batch-size 32\
 --start-eval 20\
 --eval-freq 10\
 --load-weights PATH_TO_EXPERIMENT_FOLDER/market_PCB/model.pth.tar-60\
 --train-sampler RandomIdentitySampler\
 --reg-matching-score-epoch 0\
 --graph-matching
 --max-epoch 30
 --part-score

Trained PCB model and PVPM model can be found here(code:64zy)

EXTRA

To test

Set --workers to 0, or pin_memory error will be occured.
-evaluate must be here.
--load_weights should be path of download models.

Then you can python scripts/main.py

Evaluation Results

Pretrain_PCB_model.pth.tar-60

Market1501 to Market1501
PVPM_model.pth.tar-30

Market1501 to Occlusion_reid and partial_reid

Pretrain_PCB_model.pth.tar-60

Market1501 to Market1501

--root
PATH_TO_DATAROOT
-s
market1501
-t
market1501
--save-dir
PATH_TO_EXPERIMENT_FOLDER/market_PCB
-a
pcb_p6
--gpu-devices
0
--fixbase-epoch
0
--open-layers
classifier
fc
--new-layers
classifier
em
--transforms
random_flip
--evaluate
--start-eval
60
--load-weights
PATH_TO_EXPERIMENT_FOLDER/market_PCB/Pretrain_PCB_model.pth.tar-60
--optim
sgd
--lr
0.02
--stepsize
25
50
--staged-lr
--height
384
--width
192
--batch-size
128
--base-lr-mult
0.5
--workers
0

Results

** Arguments **
adam_beta1: 0.9
adam_beta2: 0.999
app: image
arch: pcb_p6
base_lr_mult: 0.5
batch_size: 128
combineall: False
cuhk03_classic_split: False
cuhk03_labeled: False
dist_metric: euclidean
eval_freq: -1
evaluate: True
fixbase_epoch: 0
gamma: 0.1
gpu_devices: 0
graph_matching: False
height: 384
label_smooth: False
load_pose: False
load_weights: D:/weights_results/PVPM/pretrained_models/Pretrain_PCB_model.pth.tar-60
loss: softmax
lr: 0.02
lr_scheduler: multi_step
margin: 0.3
market1501_500k: False
max_epoch: 60
momentum: 0.9
new_layers: ['classifier', 'em']
no_pretrained: False
normalize_feature: False
num_att: 6
num_instances: 4
open_layers: ['classifier', 'fc']
optim: sgd
part_score: False
pooling_method: avg
print_freq: 20
ranks: [1, 3, 5, 10, 20]
reg_matching_score_epoch: 5
rerank: False
resume: 
rmsprop_alpha: 0.99
root: D:/datasets/ReID_dataset
sample_method: evenly
save_dir: D:/weights_results/PVPM/market_PCB
seed: 1
seq_len: 15
sgd_dampening: 0
sgd_nesterov: False
sources: ['market1501']
split_id: 0
staged_lr: True
start_epoch: 0
start_eval: 60
stepsize: [25, 50]
targets: ['market1501']
train_sampler: RandomSampler
transforms: ['random_flip']
use_att_loss: False
use_avai_gpus: False
use_cpu: False
use_metric_cuhk03: False
visrank: False
visrank_topk: 20
weight_decay: 0.0005
weight_t: 1
weight_x: 0
width: 192
workers: 0


Collecting env info ...
** System info **
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Microsoft Windows 10 企业版
GCC version: Could not collect
CMake version: Could not collect

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.105
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy==1.16.2
[pip] numpydoc==0.8.0
[pip] torch==1.4.0
[pip] torchreid==0.8.1
[pip] torchstat==0.0.7
[pip] torchsummary==1.5.1
[pip] torchvision==0.4.2
[conda] blas                      1.0                         mkl  
[conda] mkl                       2019.3                      203  
[conda] mkl-service               1.1.2            py37hb782905_5  
[conda] mkl_fft                   1.0.10           py37h14836fe_0  
[conda] mkl_random                1.0.2            py37h343c172_0  
[conda] torch                     1.4.0                    pypi_0    pypi
[conda] torchreid                 0.8.1                     dev_0    <develop>
[conda] torchstat                 0.0.7                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchvision               0.4.2                    pypi_0    pypi
        Pillow (5.4.1)

Building train transforms ...
+ resize to 384x192
+ random flip
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Building test transforms ...
+ resize to 384x192
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
=> Loading train (source) dataset
=> Loaded Market1501
  ----------------------------------------
  subset   | # ids | # images | # cameras
  ----------------------------------------
  train    |   751 |    12936 |         6
  query    |   750 |     3368 |         6
  gallery  |   751 |    15913 |         6
  ----------------------------------------
=> Loading test (target) dataset
=> Loaded Market1501
  ----------------------------------------
  subset   | # ids | # images | # cameras
  ----------------------------------------
  train    |   751 |    12936 |         6
  query    |   750 |     3368 |         6
  gallery  |   751 |    15913 |         6
  ----------------------------------------


  **************** Summary ****************
  train            : ['market1501']
  # train datasets : 1
  # train ids      : 751
  # train images   : 12936
  # train cameras  : 6
  test             : ['market1501']
  *****************************************


Building model: pcb_p6
Successfully loaded pretrained weights from "D:/weights_results/PVPM/pretrained_models/Pretrain_PCB_model.pth.tar-60"
Building softmax-engine for image-reid
##### Evaluating market1501 (source) #####
Extracting features from query set ...
Done, obtained 3368-by-12288 matrix
Extracting features from gallery set ...
Done, obtained 15913-by-12288 matrix
Speed: 0.0929 sec/batch
Computing distance matrix with metric=euclidean ...
Computing CMC and mAP ...
** Results **
mAP: 73.4%
CMC curve
Rank-1  : 90.6%
Rank-3  : 95.0%
Rank-5  : 96.6%
Rank-10 : 97.6%
Rank-20 : 98.5%

PVPM_model.pth.tar-30

Market1501 to Occlusion_reid and Partial_reid

--evaluate
--start-eval
30
--load-pose
--root
D:/datasets/ReID_dataset
-s
market1501
-t
occlusion_reid
partial_reid
--save-dir
D:/weights_results/PVPM/occlusion_reid_and_partial_reid
-a
pose_p6s
--gpu-devices
0
--fixbase-epoch
30
--open-layers
pose_subnet
--new-layers
pose_subnet
--transforms
random_flip
--optim
sgd
--lr
0.02
--stepsize
15
25
--staged-lr
--height
384
--width
128
--batch-size
128
--start-eval
20
--eval-freq
10
--load-weights
D:/weights_results/PVPM/pretrained_models/PVPM_model.pth.tar-30
--train-sampler
RandomIdentitySampler
--reg-matching-score-epoch
0
--graph-matching
--max-epoch
30
--part-score
--workers
0

Results

** Arguments **
adam_beta1: 0.9
adam_beta2: 0.999
app: image
arch: pose_p6s
base_lr_mult: 0.1
batch_size: 128
combineall: False
cuhk03_classic_split: False
cuhk03_labeled: False
dist_metric: euclidean
eval_freq: 10
evaluate: True
fixbase_epoch: 30
gamma: 0.1
gpu_devices: 0
graph_matching: True
height: 384
label_smooth: False
load_pose: True
load_weights: D:/weights_results/PVPM/pretrained_models/PVPM_model.pth.tar-30
loss: softmax
lr: 0.02
lr_scheduler: multi_step
margin: 0.3
market1501_500k: False
max_epoch: 30
momentum: 0.9
new_layers: ['pose_subnet']
no_pretrained: False
normalize_feature: False
num_att: 6
num_instances: 4
open_layers: ['pose_subnet']
optim: sgd
part_score: True
pooling_method: avg
print_freq: 20
ranks: [1, 3, 5, 10, 20]
reg_matching_score_epoch: 0
rerank: False
resume: 
rmsprop_alpha: 0.99
root: D:/datasets/ReID_dataset
sample_method: evenly
save_dir: D:/weights_results/PVPM
seed: 1
seq_len: 15
sgd_dampening: 0
sgd_nesterov: False
sources: ['market1501']
split_id: 0
staged_lr: True
start_epoch: 0
start_eval: 20
stepsize: [15, 25]
targets: ['occlusion_reid', 'partial_reid']
train_sampler: RandomIdentitySampler
transforms: ['random_flip']
use_att_loss: False
use_avai_gpus: False
use_cpu: False
use_metric_cuhk03: False
visrank: False
visrank_topk: 20
weight_decay: 0.0005
weight_t: 1
weight_x: 0
width: 128
workers: 0


Collecting env info ...
** System info **
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Microsoft Windows 10 企业版
GCC version: Could not collect
CMake version: Could not collect

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.105
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] numpy==1.16.2
[pip] numpydoc==0.8.0
[pip] torch==1.4.0
[pip] torchreid==0.8.1
[pip] torchstat==0.0.7
[pip] torchsummary==1.5.1
[pip] torchvision==0.4.2
[conda] blas                      1.0                         mkl  
[conda] mkl                       2019.3                      203  
[conda] mkl-service               1.1.2            py37hb782905_5  
[conda] mkl_fft                   1.0.10           py37h14836fe_0  
[conda] mkl_random                1.0.2            py37h343c172_0  
[conda] torch                     1.4.0                    pypi_0    pypi
[conda] torchstat                 0.0.7                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchvision               0.4.2                    pypi_0    pypi
        Pillow (5.4.1)

Building train transforms ...
+ resize to 384x128
+ random flip
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Building test transforms ...
+ resize to 384x128
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
=> Loading train (source) dataset
=> Loaded Market1501
  ----------------------------------------
  subset   | # ids | # images | # cameras
  ----------------------------------------
  train    |   751 |    12936 |         6
  query    |   750 |     3368 |         6
  gallery  |   751 |    15913 |         6
  ----------------------------------------
=> Loading test (target) dataset
=> Loaded Occluded_REID
  ----------------------------------------
  subset   | # ids | # images | # cameras
  ----------------------------------------
  train    |     0 |        0 |         0
  query    |   200 |     1000 |         1
  gallery  |   200 |     1000 |         1
  ----------------------------------------
=> Loaded Paritial_REID
  ----------------------------------------
  subset   | # ids | # images | # cameras
  ----------------------------------------
  train    |     0 |        0 |         0
  query    |    60 |      300 |         1
  gallery  |    60 |      300 |         1
  ----------------------------------------


  **************** Summary ****************
  train            : ['market1501']
  # train datasets : 1
  # train ids      : 751
  # train images   : 12936
  # train cameras  : 6
  test             : ['occlusion_reid', 'partial_reid']
  *****************************************


Building model: pose_p6s
Successfully loaded pretrained weights from "D:/weights_results/PVPM/pretrained_models/PVPM_model.pth.tar-30"
Building softmax-engine for image-reid
##### Evaluating occlusion_reid (target) #####
Extracting features from query set ...
Done, obtained 1000-by-2048 matrix
Extracting features from gallery set ...
Done, obtained 1000-by-2048 matrix
Speed: 1.1385 sec/batch
Computing distance matrix with metric=euclidean ...
Computing CMC and mAP ...
** Results **
mAP: 60.2%
CMC curve
Rank-1  : 67.2%
Rank-3  : 77.6%
Rank-5  : 82.4%
Rank-10 : 88.3%
Rank-20 : 92.3%
##### Evaluating partial_reid (target) #####
Extracting features from query set ...
Done, obtained 300-by-2048 matrix
Extracting features from gallery set ...
Done, obtained 300-by-2048 matrix
Speed: 0.9049 sec/batch
Computing distance matrix with metric=euclidean ...
Computing CMC and mAP ...
** Results **
mAP: 71.4%
CMC curve
Rank-1  : 75.3%
Rank-3  : 85.0%
Rank-5  : 88.7%
Rank-10 : 92.3%
Rank-20 : 96.3%

Citation

If you find this code useful to your research, please cite the following paper:

@inproceedings{gao2020pose,
title={Pose-guided Visible Part Matching for Occluded Person ReID},
author={Gao, Shang and Wang, Jingya and Lu, Huchuan and Liu, Zimo},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11744--11752},
year={2020}
}

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
docs		docs
scripts		scripts
torchreid		torchreid
.gitignore		.gitignore
README.md		README.md
bibtex		bibtex
requirements.txt		requirements.txt
setup.py		setup.py

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CVPR2020 Pose-guided Visible Part Matching for Occluded Person ReID

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Pose extraction

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Pretrain_PCB_model.pth.tar-60

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CVPR2020 Pose-guided Visible Part Matching for Occluded Person ReID

Contact

Modification

Supplementary Specification

Compile

Dependencies

Related Project

Dataset Preparation

Pose extraction

To Train PCB baseline

To train PVPM

EXTRA

To test

Evaluation Results

Pretrain_PCB_model.pth.tar-60

Market1501 to Market1501

Results

PVPM_model.pth.tar-30

Market1501 to Occlusion_reid and Partial_reid

Results

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