This repository is an official PyTorch implementation of our paper "Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN". Accepted by IEEE TRANSACTIONS ON INTELLIGENCE TRANSPORTATION SYSTEMS, 2023. (IF: 9.551)
cuda == 10.2
Python == 3.6.4
Pytorch == 1.8.0+cu101
# clone this repository
git clone https://github.com/XU-GITHUB-curry/LETNet_Lightweight-Real-time-Semantic-Segmentation-Network-with-Efficient-Transformer-and-CNN.git
# cityscapes
python train.py --dataset cityscapes --train_type train --max_epochs 1000 --lr 4.5e-2 --batch_size 5
# camvid
python train.py --dataset cityscapes --train_type train --max_epochs 1000 --lr 1e-3 --batch_size 8
# cityscapes
python test.py --dataset cityscapes --checkpoint ./checkpoint/cityscapes/FBSNetbs4gpu1_train/model_1000.pth
# camvid
python test.py --dataset camvid --checkpoint ./checkpoint/camvid/FBSNetbs6gpu1_trainval/model_1000.pth
only for cityscapes dataset
python predict.py --dataset cityscapes
- Please refer to our article for more details.
Methods | Dataset | Input Size | mIoU(%) |
---|---|---|---|
LETNet | Cityscapes | 512x1024 | 72.8 |
LETNet | CamVid | 360x480 | 70.5 |
If you find this project useful for your research, please cite our paper:
@article{xu2023lightweight,
title={Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN},
author={Xu, Guoan and Li, Juncheng and Gao, Guangwei and Lu, Huimin and Yang, Jian and Yue, Dong},
journal={IEEE Transactions on Intelligent Transportation Systems},
year={2023},
publisher={IEEE}
}
@misc{Efficient-Segmentation-Networks,
author = {Yu Wang},
title = {Efficient-Segmentation-Networks Pytorch Implementation},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks}},
commit = {master}
}
For more code about lightweight real-time semantic segmentation, please refer to: https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks