jegernoutt / swin-transformer-semantic-segmentation

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

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}
}

Other Links

Image Classification: See Swin Transformer for Image Classification.

Object Detection: See Swin Transformer for Object Detection.