This is a PyTorch implementation of Swin MAE.
- Install the required environment in "requirements.txt".
- Open "train.py" and fill in the dataset path. There should be at least one category folder under this path. The data for training is stored in the category folder.
- Run "train.py".
@article{ WOS:001012921200001,
Author = {Xu, Zi'an and Dai, Yin and Liu, Fayu and Chen, Weibing and Liu, Yue and
Shi, Lifu and Liu, Sheng and Zhou, Yuhang},
Title = {Swin MAE: Masked autoencoders for small datasets},
Journal = {COMPUTERS IN BIOLOGY AND MEDICINE},
Year = {2023},
Volume = {161},
Month = {JUL},
DOI = {10.1016/j.compbiomed.2023.107037},
EarlyAccessDate = {MAY 2023},
Article-Number = {107037},
ISSN = {0010-4825},
EISSN = {1879-0534},
ORCID-Numbers = {Sheng, Liu/0000-0002-5251-2767
Xu, Zi'an/0000-0002-6374-1805},
Unique-ID = {WOS:001012921200001},
}