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Ranking Models in Unlabeled New Environments

Prerequisites

This code uses the following libraries

• Python 3.7
• NumPy
• PyTorch 1.7.0 + torchivision 0.8.1
• Sklearn
• Scipy 1.2.1

the environment can be created by using "proxy_set.yml" :

conda env create -f proxy_set.yml 

Data Preparation

The folder of each dataset (take Market-1501 as an example) in the data pool should look like this:

Market-1501
├── bounding_box_train/  # the traning set is only necessary for target dataset
│   └── ...
├── bounding_box_test/ 
│   └── ...
└── query/
    └── ...

Run the Code

searching data

python dataset_selection.py --weight 0.6 --result_dir 'sample_data/'

Searched data will be saved in "result_dir". Other parameters, such as the number of clusters, can be set in dataset_selection.py.

Citation

Please cite this paper if it helps your research:

@inproceedings{sun2021,
  title={Ranking Models in Unlabeled New Environments},
  author={Sun, Xiaoxiao and Hou, Yunzhong and Deng, Weijian and Li, Hongdong and Zheng, Liang},
  booktitle={IEEE Conference on International Conference on Computer Vision (ICCV)},
  year={2021}
}