This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

Related tags

Deep LearningPFD_Net
Overview

PFD：Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer

Python >=3.6 PyTorch >=1.6

This repo is the official implementation of "Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer(PFD), Tao Wang, Hong Liu, Pinghao Song, Tianyu Guo& Wei Shi" in PyTorch.

Pipeline

framework

Dependencies

  • timm==0.3.2

  • torch==1.6.0

  • numpy==1.20.2

  • yacs==0.1.8

  • opencv_python==4.5.2.54

  • torchvision==0.7.0

  • Pillow==8.4.0

Installation

pip install -r requirements.txt

If you find some packages are missing, please install them manually.

Prepare Datasets

mkdir data

Please download the dataset, and then rename and unzip them under the data

data
|--market1501
|
|--Occluded_Duke
|
|--Occluded_REID
|
|--MSMT17
|
|--dukemtmcreid

Prepare ViT Pre-trained and HRNet Pre-trained Models

mkdir data

The ViT Pre-trained model can be found in ViT_Base, The HRNet Pre-trained model can be found in HRNet, please download it and put in the './weights' dictory.

Training

We use One GeForce GTX 1080Ti GPU for Training Before train the model, please modify the parameters in config file, please refer to Arguments in TransReID

python occ_train.py --config_file {config_file path}
#example
python occ_train.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'

Test the model

First download the Occluded-Duke model:Occluded-Duke

To test on pretrained model on Occ-Duke: Modify the pre-trained model path (PRETRAIN_PATH:ViT_Base, POSE_WEIGHT:HRNet, WEIGHT:Occluded-Duke) in yml, and then run:

## OccDuke for example
python test.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'

Occluded-Duke Results

Model Image Size Rank-1 mAP
HOReID 256*128 55.1 43.8
PAT 256*128 64.5 53.6
TransReID 256*128 64.2 55.7
PFD 256*128 67.7 60.1
TransReID* 256*128 66.4 59.2
PFD* 256*128 69.5 61.8

$*$means the encoder is with a small step sliding-window setting

Occluded-REID Results

Model Image Size Rank-1 mAP
HOReID 256*128 80.3 70.2
PAT 256*128 81.6 72.1
PFD 256*128 79.8 81.3

Market-1501 Results

Model Image Size Rank-1 mAP
HOReID 256*128 80.3 70.2
PAT 256*128 95.4 88.0
TransReID 256*128 95.4 88.0
PFD 256*128 95.5 89.6

Citation

If you find our work useful in your research, please consider citing this paper! (preprint version will be available soon)

@inproceedings{wang2022pfd,
  Title= {Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer},
  Author= {Tao Wang, Hong Liu, Pinhao Song, Tianyu Guo and Wei Shi},
  Booktitle= {AAAI},
  Year= {2022}
}

Acknowledgement

Our code is extended from the following repositories. We thank the authors for releasing the codes.

License

This project is licensed under the terms of the MIT license.

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Comments
  • 精度达不到论文里面的数据

    精度达不到论文里面的数据

    作者您好，我在1501上测试了一下 就改了 /home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/configs/Market1501/skeleton_pfd.yml 这个文件，里面的参数并没有改动 改了权重的路径，和文件夹的路径 其他都没变，如何训练300轮次后 我选择最高300轮的 /home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/logs/Market/pfd_net/skeleton_transformer_300.pth 去测试 结果是 ： 2021-12-28 18:23:39,417 PFDreid.test INFO: Validation Results 2021-12-28 18:23:39,417 PFDreid.test INFO: mAP: 88.2% 2021-12-28 18:23:39,418 PFDreid.test INFO: CMC curve, Rank-1 :94.8% 2021-12-28 18:23:39,418 PFDreid.test INFO: CMC curve, Rank-5 :98.3% 2021-12-28 18:23:39,418 PFDreid.test INFO: CMC curve, Rank-10 :99.0% 达不到论文的95.5 甚至不如TransReID的精度 ？？？ 您能看看是为什么嘛？

    MODEL: PRETRAIN_CHOICE: 'imagenet' PRETRAIN_PATH: '/home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/weights/jx_vit_base_p16_224-80ecf9dd.pth' METRIC_LOSS_TYPE: 'triplet' IF_LABELSMOOTH: 'on' IF_WITH_CENTER: 'no' NAME: 'skeleton_transformer' NO_MARGIN: True DEVICE_ID: ('2') TRANSFORMER_TYPE: 'vit_base_patch16_224_TransReID' STRIDE_SIZE: [16, 16]

    SIE_CAMERA: True SIE_COE: 3.0 JPM: True RE_ARRANGE: True NUM_HEAD: 8 DECODER_DROP_RATE: 0.1 DROP_FIRST: False NUM_DECODER_LAYER: 6 QUERY_NUM: 17 POSE_WEIGHT: '/home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/weights/pose_hrnet_w48_384x288.pth' SKT_THRES: 0.2

    INPUT: SIZE_TRAIN: [256, 128] SIZE_TEST: [256, 128] PROB: 0.5 # random horizontal flip RE_PROB: 0.5 # random erasing PADDING: 10 PIXEL_MEAN: [0.5, 0.5, 0.5] PIXEL_STD: [0.5, 0.5, 0.5]

    DATASETS: NAMES: ('market1501') ROOT_DIR: ('/home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/data/')

    DATALOADER: SAMPLER: 'softmax_triplet' NUM_INSTANCE: 4 NUM_WORKERS: 8

    SOLVER: OPTIMIZER_NAME: 'SGD' MAX_EPOCHS: 300 BASE_LR: 0.008 IMS_PER_BATCH: 64 WARMUP_METHOD: 'linear' LARGE_FC_LR: False CHECKPOINT_PERIOD: 60 LOG_PERIOD: 50 EVAL_PERIOD: 30 WEIGHT_DECAY: 1e-4 WEIGHT_DECAY_BIAS: 1e-4 BIAS_LR_FACTOR: 2

    TEST: EVAL: True IMS_PER_BATCH: 256 RE_RANKING: False WEIGHT: "/home/zqx_3090/PersonReID/PersonReID2/PFD_Net-master/logs/Market/pfd_net/skeleton_transformer_300.pth" #put your own pth NECK_FEAT: 'before' FEAT_NORM: 'yes'

    OUTPUT_DIR: 'logs/Market/pfd_net'

    opened by zqx951102 3
  • 使用您的Occluded-Duke的预训练模型达不到文中的结果

    使用您的Occluded-Duke的预训练模型达不到文中的结果

    作者您好： 感谢你做出如此优秀的工作，我按照reademe的要求在使用您的Occluded-Duke的预训练模型时，发现达不到文中所说的结果，下图是我测试的结果： image 跟论文中的结果大约相差2%，我使用的时pytorch1.7.1, cuda10.2, python3.7.13;所以我想知道这是什么原因造成的呢？ 期待您的回复。

    opened by changshuowang 2
  • There is no Occlude-REID data loader

    There is no Occlude-REID data loader

    Good work! I respect your contributions!

    I want to testing Occluded-REID dataset in your code, but there is no loader. In your code, dataset.make_dataloader.py, line 14 "from .occ_reid import Occluded_REID"

    Would you share this code?

    thank you

    opened by intlabSeJun 4
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