Notification

We modified the original project for our usage of railway train detection.

Transformer is used to achieve semantic segmantation, and has been applied in practical project.

Lawin Transformer

Paper

Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention (Under Review).

Installation

For install and data preparation, please refer to the guidelines in MMSegmentation v0.13.0.

Important requirements: CUDA 11.6 and pytorch 1.8.1

pip install torchvision==0.9.1
pip install timm==0.3.2
pip install mmcv-full==1.2.7
pip install opencv-python==4.5.1.48
pip install einops
cd lawin && pip install -e . --user

Evaluation

Download trained models.

# Single-gpu testing
python tools/test.py local_configs/segformer/B2/lawin.b2.512x512.ade.160k.py /path/to/checkpoint_file

Training

Download weights pretrained on ImageNet-1K, and put them in a folder pretrained/.

Example: train lawin-B2 on ADE20K:

# Multi-gpu training
./tools/dist_train.sh local_configs/segformer/B2/lawin.b2.512x512.ade.160k.py <GPU_NUM> --work-dir <WORK_DIRS_TO_SAVE_WEIGHTS&LOGS> --options evaluation.interval=320000

Citation

@article{yan2022lawin,
  title={Lawin transformer: Improving semantic segmentation transformer with multi-scale representations via large window attention},
  author={Yan, Haotian and Zhang, Chuang and Wu, Ming},
  journal={arXiv preprint arXiv:2201.01615},
  year={2022}
}