MetaSeg: MetaFormer-Based Global Contexts-Aware Network for Efficient Semantic Segmentation (Accepted by WACV 2024)

📝Paper

Beoungwoo Kang*, Seunghun Moon*, Yubin Cho*, Hyunwoo Yu*, Suk-ju Kang^†

* Equal contribution, ^†Correspondence

Sogang University

The official code is available at here.

Installation

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

pip install timm
cd MetaSeg
python setup.py develop

Training

Download backbone [ MSCAN-T & MSCAN-B ] pretrained weights.

Put them in a folder pretrain/.

Example - Train MetaSeg-T on ADE20K:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./tools/dist_train.sh local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py <GPU_NUM>

Evaluation

Example - Evaluate MetaSeg-T on ADE20K:

# Single-gpu testing
CUDA_VISIBLE_DEVICES=0 python tools/test.py local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py /path/to/checkpoint_file

# Multi-gpu testing
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./tools/dist_test.sh local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>

Citation

@inproceedings{kang2024metaseg,
  title={MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation},
  author={Kang, Beoungwoo and Moon, Seunghun and Cho, Yubin and Yu, Hyunwoo and Kang, Suk-Ju},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={434--443},
  year={2024}
}