ycszen / Swin-Transformer-Semantic-Segmentation

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

Home Page:https://arxiv.org/abs/2103.14030

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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:

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.

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About

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

https://arxiv.org/abs/2103.14030

License:Apache License 2.0


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