- python == 3.6.8
- cudatoolkit == 11.0
- pytorch ==1.7.1
- torchvision == 0.8.2
- numpy, scipy, sklearn, PIL, argparse, tqdm
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Configure the pytorch environment
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Download the dataset and write the correspinding text files via https://github.com/tim-learn/Generate_list (check the file path in the 'data/office-home/**_list.txt' folder)
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Run the following code (reproduce the results for ResNet-50 (source backbone) -> ResNet-50 (target backbone) in upper Table 2)
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training the source model first
python DINE_dist.py --gpu_id 0 --seed 2021 --output_src ./ckps/src --dset office-home --s 0 --da uda --net_src resnet50 --max_epoch 50
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the first step (Distill)
python DINE_dist.py --gpu_id 0 --seed 2021 --output_src ./ckps/src --dset office-home --s 0 --da uda --net_src resnet50 --max_epoch 30 --net resnet50 --output ./ckps/tar --distill --topk 1
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the second step (Finetune)
python DINE_ft.py --gpu_id 0 --seed 2021 --dset office-home --s 0 --da uda --net_src resnet50 --max_epoch 30 --net resnet50 --lr 1e-2 --output ./ckps/tar
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If you find this code useful for your research, please cite our paper
@inproceedings{liang2022dine,
title={DINE: Domain Adaptation from Single and Multiple Black-box Predictors},
author={Liang, Jian and Hu, Dapeng and Feng, Jiashi and He, Ran},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}