MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation (Accepted by WACV 2024)
The code is based on MMSegmentaion v0.24.1.
For install and data preparation, please refer to the guidelines in MMSegmentation.
pip install timm
cd MetaSeg
python setup.py develop
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>
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>