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Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation

Get start

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]

Citing Graph-Segmenter

@article{liu2021Swin,
  title={Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation},
  author={Zizhang Wu and Jun Li and Yuanzhu Gan and Muqing Fang and Tianhao Xu and Fan Wang},
  year={2022}
}

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