[Paper] [Poster] [PPT slides]
Qiming Hu, Hainuo Wang, Xiaojie Guo
College of Intelligence and Computing, Tianjin University
pip install -r requirements.txt
- 7,643 images from the Pascal VOC dataset, center-cropped as 224 x 224 slices to synthesize training pairs;
- 90 real-world training pairs provided by Zhang et al.;
- 200 real-world training pairs provided by IBCLN (In our training setting 2, † labeled in our paper).
- 45 real-world testing images from CEILNet dataset;
- 20 real testing pairs provided by Zhang et al.;
- 20 real testing pairs provided by IBCLN;
- 454 real testing pairs from SIR^2 dataset, containing three subsets (i.e., Objects (200), Postcard (199), Wild (55)).
Download all in one by Google Drive or 百度云.
Setting I (w/o Nature): python train_sirs_setting1.py --name dsit_large_train_setting1 --arch dsit_large --model dsit_model_sirs_lrm --dataset sirs_dataset --loss losses --lambda_vgg 0.01 --lambda_rec 0.2 --size_rounded --seed 2024 --backbone_weight_path "./weights/swin_large_o365_finetune.pth" --base_dir "YOUR_DATA_DIR]" --batchSize 1
Setting II (w/ Nature): python train_sirs_setting2.py --name dsit_large_train_setting2 --arch dsit_large --model dsit_model_sirs_lrm --dataset sirs_dataset --loss losses --lambda_vgg 0.01 --lambda_rec 0.2 --size_rounded --seed 2024 --backbone_weight_path "./weights/swin_large_o365_finetune.pth" --base_dir "[YOUR_DATA_DIR]" --batchSize 1
Setting I (w/o Nature): python eval_sirs.py --name dsit_large_setting1_eval_epoch20 --arch dsit_large --model dsit_model_sirs_lrm --dataset sirs_dataset --size_rounded --weight_path "./weights/dist-large-setting1-epoch20.pth" --backbone_weight_path "./weights/swin_large_o365_finetune.pth" --base_dir "[YOUR_DATA_DIR]"
Setting II (w/ Nature): eval_sirs.py --name dsit_large_setting2_eval_epoch66 --arch dsit_large --model dsit_model_sirs_lrm --dataset sirs_dataset --size_rounded --test_nature --weight_path "./weights/dist-large-setting2-epoch66.pth" --backbone_weight_path "./weights/swin_large_o365_finetune.pth" --base_dir "[YOUR_DATA_DIR]"
python test_sirs.py --name dsit_large_setting2_test_epoch66 --arch dsit_large --model dsit_model_sirs_lrm --dataset sirs_dataset --size_rounded --test_nature --weight_path "./weights/dist-large-setting2-epoch66.pth" --backbone_weight_path "./weights/swin_large_o365_finetune.pth" --test_dir "[YOUR_TESTING_DATA_DIR]"
Download the trained weights by Google Drive or 百度云 and drop them into the "weights" dir.
