Official Implementation for ProxyMix
[ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation]
This implementation is based on ATDOC.
Framework:
- train on the source domain;
- Construct the proxy source domain and train on target dataset.
Prerequisites:
- python == 3.6.8
- pytorch ==1.1.0
- torchvision == 0.3.0
- numpy, scipy, sklearn, PIL, argparse, tqdm
Dataset:
- Please manually download the datasets Office, Office-Home, VisDA-C from the official websites, and modify the path of images in each ‘.txt’ under the folder ‘./data/’. [**How to generate such txt files could be found in https://github.com/tim-learn/Generate_list **]
Training:
-
Office-31 dataset
# train source model python train_source.py --dset office --s 0 --max_epoch 50 # train target model python train_target.py --dset office --easynum 5 --output test --gpu_id 7 --s 0 --t 1 python train_target.py --dset office --easynum 5 --output test --gpu_id 7 --s 0 --t 2
-
Office-Home dataset
- Coming soon …
-
VISDA-C dataset
- Coming soon …
Citation
If you find this code useful for your research, please cite our papers
Coming soon...