BMD
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[ECCV 2022] The official repository of our paper "BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation"
BMD: Class-balanced Multicentric Dynamic Prototype Strategy for SFDA [ECCV 2022].
Attention: Our new work on source-free universal domain adaptation has been accepted by CVPR-2023! The paper "Upcycling Models under Domain and Category Shift" is available at https://arxiv.org/abs/2303.07110. The code also has been made public at https://github.com/ispc-lab/GLC.
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.
Prerequisites
- python3
- pytorch >= 1.7.0
- torchvision
- numpy, scipy, sklearn, PIL, argparse, tqdm, wandb
Step
- Please first prepare the pytorch enviroment.
- Please download the VisDA-C dataset from the official website, and then unzip the dataset to the
./data
folder. - Prepare the source model by running following command
sh ./scripts/train_soruce.sh
- 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
Acknowledgement
This codebase is based on SHOT-ICML2020.
Citation
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}
}