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Showing content from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation below:

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

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

05/11/2021 Models for MoBY are released

04/12/2021 Initial commits

Notes:

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

Notes:

• The learning rate needs to be tuned for best practice.
• MoBY pre-trained models can be downloaded from MoBY with Swin Transformer.

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

# 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

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.

@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}
}

Image Classification: See Swin Transformer for Image Classification.

Object Detection: See Swin Transformer for Object Detection.

Self-Supervised Learning: See MoBY with Swin Transformer.

Video Recognition, See Video Swin Transformer.

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