CST
Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021)
Prerequisites
- torch>=1.7.0
- torchvision
- qpsolvers
- numpy
- prettytable
- tqdm
- scikit-learn
- webcolors
- matplotlib
Training
VisDA-2017
CUDA_VISIBLE_DEVICES=0 python run_cst.py data/visda-2017 -d VisDA2017 -s Synthetic -t Real -a resnet101 \
--epochs 30 --early 12 --lr 0.002 --per-class-eval --temperature 3.0 --center-crop --log logs/cst/VisDA2017 \
--trade-off 0.08 trade-off1 2.0 --trade-off3 0.5 --threshold 0.97 -b 28
Office Home
CUDA_VISIBLE_DEVICES=0 python run_cst.py data/office-home -d OfficeHome -s Pr -t Rw -a resnet50 \
--epochs 30 --early 30 --temperature 2.5 --bottleneck-dim 2048 --log logs/cst/OfficeHome_Pr2Rw \
--trade-off1 2.0 --trade-off3 0.5 --threshold 0.97 --trade-off 0.015
Acknowledgement
This code is implemented based on the Transfer Learning Library, and it is our pleasure to acknowledge their contributions.
The SAM code is adapted from https://github.com/davda54/sam.
Citation
If you use this code for your research, please consider citing:
@article{liu2021cycle,
title={Cycle Self-Training for Domain Adaptation},
author={Liu, Hong and Wang, Jianmin and Long, Mingsheng},
journal={arXiv preprint arXiv:2103.03571},
year={2021}
}
Contact
If you have any problem about our code, feel free to contact
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