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The official repository of our paper "BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation". Here, we present the demo implementation on VisDA-C dataset.

• python3
• pytorch >= 1.7.0
• torchvision
• numpy, scipy, sklearn, PIL, argparse, tqdm, wandb

1. Please first prepare the pytorch enviroment.
2. Please download the VisDA-C dataset from the official website, and then unzip the dataset to the ./data folder.
3. Prepare the source model by running following command

   sh ./scripts/train_soruce.sh

4. Perform the target model adaptation by running following command. Please note that you need to first assign the source model checkpoint path in the ./scripts/train_target.sh script.

   sh ./scripts/train_target.sh

This codebase is based on SHOT-ICML2020.

If you find it helpful, please consider citing:

@inproceedings{sanqing2022BMD,
  title={BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation},
  author={Sanqing Qu, Guang Chen, Jing Zhang, Zhijun Li, Wei He, Dacheng Tao},
  booktitle={European conference on computer vision},
  year={2022}
}