Labes problem

[DuyunliangToon]

Hi, I have a question about tags while reproducing your paper. According to the description in your dataset configuration file, train_source_real, train_source_fake and test_traget_real require labels, and train_target_real and train_target_fake do not require labels. But in my actual training, I found that train_source_fake does not need labels, and train_target_real needs labels. From this point of view, it requires all the labels of the two data sets, which is not the so-called semi-supervised training. Not sure if I made a mistake, hope you have time to help me out.

[hnuzhy]

Yes, we do use labels of images in train_source_real and train_source_fake, and not use labels of images in train_target_real and train_target_fake. When applying dataloading, the train_source_fake has exactly the same labels as train_source_real, thus we do not need load its labels one more time. As for labels of train_target_real and train_target_fake, we do not use them when training, You can refer the training code in lines list below
https://github.com/hnuzhy/SSDA-YOLO/blob/master/ssda_yolov5_train.py#L489~#L534
We want these labels in target domain just for training of Oracle experiments, which are fully supervised training and testing of the target domain.

[DuyunliangToon]

Thanks for your guidance.