mmsegmentation/configs/swin

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Introduction

@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}
}

Usage

To use other repositories' pre-trained models, it is necessary to convert keys.

We provide a script swin2mmseg.py in the tools directory to convert the key of models from the official repo to MMSegmentation style.

python tools/model_converters/swin2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}

E.g.

python tools/model_converters/swin2mmseg.py https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth pretrain/swin_base_patch4_window7_224.pth

This script convert model from PRETRAIN_PATH and store the converted model in STORE_PATH.

Results and models

ADE20K

Method	Backbone	Crop Size	pretrain	pretrain img size	Batch Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
UperNet	Swin-T	512x512	ImageNet-1K	224x224	16	160000	5.02	21.06	44.41	45.79	config	model | log
UperNet	Swin-S	512x512	ImageNet-1K	224x224	16	160000	6.17	14.72	47.72	49.24	config	model | log
UperNet	Swin-B	512x512	ImageNet-1K	224x224	16	160000	7.61	12.65	47.99	49.57	config	model | log
UperNet	Swin-B	512x512	ImageNet-22K	224x224	16	160000	-	-	50.31	51.9	config	model | log
UperNet	Swin-B	512x512	ImageNet-1K	384x384	16	160000	8.52	12.10	48.35	49.65	config	model | log
UperNet	Swin-B	512x512	ImageNet-22K	384x384	16	160000	-	-	50.76	52.4	config	model | log