Swin Transformer for Semantic Segmentaion

This repo contains the supported code and configuration files to reproduce semantic segmentaion results of Swin Transformer. It is based on mmsegmentaion.

Updates

04/12/2021 Initial commits

Results and Models

ADE20K

Backbone	Method	Crop Size	Lr Schd	mIoU	mIoU (ms+flip)	#params	FLOPs	config	log	model
Swin-T	UPerNet	512x512	160K	44.51	45.81	60M	945G	config	github/baidu	github/baidu
Swin-S	UperNet	512x512	160K	47.64	49.47	81M	1038G	config	github/baidu	github/baidu
Swin-B	UperNet	512x512	160K	48.13	49.72	121M	1188G	config	github/baidu	github/baidu

Notes:

Pre-trained models can be downloaded from Swin Transformer for ImageNet Classification.
Access code for baidu is swin.

Usage

Installation

Please refer to get_started.md for installation and dataset preparation.

Inference

# single-gpu testing
python tools/test.py <CONFIG_FILE> <SEG_CHECKPOINT_FILE> --eval mIoU

# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <SEG_CHECKPOINT_FILE> <GPU_NUM> --eval mIoU

# multi-gpu, multi-scale testing
tools/dist_test.sh <CONFIG_FILE> <SEG_CHECKPOINT_FILE> <GPU_NUM> --aug-test --eval mIoU

Training

To train with pre-trained models, run:

# single-gpu training
python tools/train.py <CONFIG_FILE> --options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]

# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]

For example, to train an UPerNet model with a Swin-T backbone and 8 gpus, run:

tools/dist_train.sh configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k.py 8 --options model.pretrained=<PRETRAIN_MODEL>

Notes:

use_checkpoint is used to save GPU memory. Please refer to this page for more details.
The default learning rate and training schedule is for 8 GPUs and 2 imgs/gpu.

Citing Swin Transformer

@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}
}

zhengsx/Swin-Transformer-Semantic-Segmentation