mmclassification/configs/swin_transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Introduction

[ALGORITHM]

@article{liu2021Swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  journal={arXiv preprint arXiv:2103.14030},
  year={2021}
}

Pretrain model

The pre-trained modles are converted from model zoo of Swin Transformer.

ImageNet 1k

Model	Pretrain	resolution	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Download
Swin-T	ImageNet-1k	224x224	28.29	4.36	81.18	95.52	model
Swin-S	ImageNet-1k	224x224	49.61	8.52	83.21	96.25	model
Swin-B	ImageNet-1k	224x224	87.77	15.14	83.42	96.44	model
Swin-B	ImageNet-1k	384x384	87.90	44.49	84.49	96.95	model
Swin-B	ImageNet-22k	224x224	87.77	15.14	85.16	97.50	model
Swin-B	ImageNet-22k	384x384	87.90	44.49	86.44	98.05	model
Swin-L	ImageNet-22k	224x224	196.53	34.04	86.24	97.88	model
Swin-L	ImageNet-22k	384x384	196.74	100.04	87.25	98.25	model

Results and models

ImageNet

Model	Pretrain	resolution	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
Swin-T	ImageNet-1k	224x224	28.29	4.36	81.18	95.61	config	model | log
Swin-S	ImageNet-1k	224x224	49.61	8.52	83.02	96.29	config	model | log
Swin-B	ImageNet-1k	224x224	87.77	15.14	83.36	96.44	config	model | log