Link to our paper: https://arxiv.org/abs/2301.13530
Please refer to requirement.txt for all required packages. Tested using Python 3.8
Then, clone this repo
git clone
cd DomainGMDCDownload pre-trained model from: AdaIN-Style-Transfer-PyTorch
Put the weights in ./adain_weights folder
Prepare datasets of interest as described in dataset.md.
Example training command for OfficeHome dataset with RealWorld, Clipart, and Product domains:
python tools/run_end_to_end.py --domain_names Product_Clipart_RealWorld --seed 0 --embedding_batch_size 512 --domain_loss_weight 0.02 --dist-url tcp://localhost:10026 --keep_strong_heads --multi_q --balance_moco_domains --data_type officehome --num_cluster 65 --center_based_truncate --wandb_run_name <enter_run_name> --epochs 500 --data ./datasets/ --root_save_folder ./results/ --use_wandb --arch resnet18 --soft_balance --domain_size_layers 2048 1024 512 256 128 --train_self_batch_size 256 --batch_size 8 --style_transfer --heads2keep 5 --p_bcd_augment 0.2 --self_smoothing 0.9 --pred_based_smoothing --moco_p_bcd_augment 0.8 The above should run both pre-training and training in one run.
An example on Officehome dataset:
python tools/evaluate.py --dir_and_regex '<Root_path>/spice/results/officehome/*Art_Clipart_Product*'The above will find all runs in "officehome" folder which were trained using "Art" "Clipart" and "Product" domains. It will automatically infer the remaining domain and perform evaluation on it.
Implemented datasets and domains are the same as in our paper:
"pacs": ["cartoon", "photo", "artpainting", "sketch"]
"officehome": ["RealWorld", "Clipart", "Product", "Art"]
"office31": ["amazon", "dslr", "webcam"]
"DomainNet": ["clipart", "infograph", "quickdraw", "painting", "real", "sketch"]
Result should include accuracy in cases it is possible to compute, otherwise it will print a list of the not predicted clusters.