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Code for L2ID CVPRW 2021 paper Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics
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Anurag14/STar-framework
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conda env create -n SSDA.yml
The code is written for Pytorch 0.4.0, but should work for other versionwith some modifications.
Download the cleaned version of the domainnet data fromhere and place them inside the './data/multi/' folder.
The images will be stored in the following way.
./data/multi/real/category_name
,
./data/multi/sketch/category_name
The dataset split files are stored as follows,
./data/txt/multi/labeled_source_images_real.txt
,
./data/txt/multi/unlabeled_target_images_sketch_3.txt
,
With regard to office and office home dataset, store the image files in the following ways,
./data/office/amazon/category_name
,./data/office_home/Real/category_name
,
We provide the split of office and office-home.
To run training using alexnet,
sh run_train.sh gpu_id method alexnet
where, gpu_id = 0,1,2,3...., method=[MME,ENT,S+T].