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Introduction
This is the code of Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification ECCV 2020.
Preparation
Requirements: Python=3.6 and Pytorch>=1.0.0
Please refer to ECN to prepare dataset, the file structure is as follow:
JVTC/data
│
└───Market-1501 OR DukeMTMC-reID
│
└───bounding_box_train
│
└───bounding_box_test
│
└───bounding_box_train_camstyle_merge
|
└───query
"bounding_box_train_camstyle_merge" dir merges the "bounding_box_train" and "bounding_box_train_camstyle" for convenience.
Training and test
We utilize 2 GTX-2080TI GPU for model training.
# Duke to Market-1501 training&evalution
python duke2market_train.py
# Duke to Market-1501 evalution with joint similarity
python duke2market_evaluate_joint_sim.py
# Market-1501 to Duke training&evalution
python market2duke_train.py
# Market-1501 to Duke evalution with joint similarity
python market2duke_evaluate_joint_sim.py
Results
References
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[1] Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. CVPR 2019.
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[2] Spatial-temporal person re-identification. AAAI 2019.
Citation
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