Implementation of NFormer: Robust Person Re-identification with Neighbor Transformer. CVPR2022
- Python3
- pytorch>=0.4
- torchvision
- pytorch-ignite=0.1.2 (Note: V0.2.0 may result in an error)
- yacs
- 1 NVIDIA 3090 Ti
Create a directory to store reid datasets under this repo or outside this repo. Set your path to the root of the dataset in config/defaults.py or set in scripts Experiment-all_tricks-tri_center-market.sh and Test-all_tricks-tri_center-feat_after_bn-cos-market.sh.
- Download dataset to
data/from https://zheng-lab.cecs.anu.edu.au/Project/project_reid.html - Extract dataset and rename to
market1501. The data structure would like:
|- data
|- market1501 # this folder contains 6 files.
|- bounding_box_test/
|- bounding_box_train/
......download the pretrained resnet50 model and set the path at line3
run Experiment-all_tricks-tri_center-market.sh to train NFormer on Market-1501 dataset
sh Experiment-all_tricks-tri_center-market.sh
or
python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haochen/workspace/project/NFORMER/')" OUTPUT_DIR "('work_dirs')"
run Test-all_tricks-tri_center-feat_after_bn-cos-market.sh to evaluate NFormer on Market-1501 dataset. Change TEST.TEST_NFORMER to determine test for NFormer ('yes') or CNNEncoder ('no').
sh Test-all_tricks-tri_center-feat_after_bn-cos-market.sh
or
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haochen/workspace/project/NFORMER')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('test/nformer_model.pth')" TEST.TEST_NFORMER "('no')"
This repo is highly based on reid-strong-baseline, thanks for their excellent work.
@article{wang2022nformer,
title={NFormer: Robust Person Re-identification with Neighbor Transformer},
author={Wang, Haochen and Shen, Jiayi and Liu, Yongtuo and Gao, Yan and Gavves, Efstratios},
journal={arXiv preprint arXiv:2204.09331},
year={2022}
}
@InProceedings{Luo_2019_CVPR_Workshops,
author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
title = {Bag of Tricks and a Strong Baseline for Deep Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
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