SegFormer
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo.
Technical Report 2021.
This repository contains the PyTorch training/evaluation code and the pretrained models for SegFormer.
SegFormer is a simple, efficient and powerful semantic segmentation method, as shown in Figure 1.
Installation
For install and data preparation, please refer to the guidelines in MMSegmentation v0.13.0.
Other requirements:
pip install timm==0.3.2
Evaluation
Download trained weights.
Example: evaluate SegFormer-B1 on ADE20K:
# Single-gpu testing
python tools/test.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file
# Multi-gpu testing
./tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>
# Multi-gpu, multi-scale testing
tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM> --aug-test
Training
Download weights pretrained on ImageNet-1K, and put them in a folder pretrained/.
Example: train SegFormer-B1 on ADE20K:
# Single-gpu training
python tools/train.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py
# Multi-gpu training
./tools/dist_train.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py <GPU_NUM>
