LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation

├── datasets  # contains all datasets for the project
|  └── cityscapes #  cityscapes dataset
|  |  └── gtCoarse #  Coarse cityscapes annotation
|  |  └── gtFine #  Fine cityscapes annotation
|  |  └── leftImg8bit #  cityscapes training image
|  └── cityscapesscripts #  cityscapes dataset label convert scripts！
├── utils
|  └── dataset.py # dataloader for cityscapes dataset
|  └── iouEval.py # for test 'iou mean' and 'iou per class'
|  └── transform.py # data preprocessing
|  └── visualize.py # Visualize with visdom 
|  └── loss.py # loss function 
├── checkpoint
|  └── xxx.pth # pretrained models encoder form ImageNet
├── save
|  └── xxx.pth # trained models form scratch 
├── imagenet-pretrain
|  └── lednet_imagenet.py # 
|  └── main.py # 
├── train
|  └── lednet.py  # model definition for semantic segmentation
|  └── main.py # train model scripts
├── test
|  |  └── dataset.py 
|  |  └── lednet.py # model definition
|  |  └── lednet_no_bn.py # Remove the BN layer in model definition
|  |  └── eval_cityscapes_color.py # Test the results to generate RGB images
|  |  └── eval_cityscapes_server.py # generate result uploaded official server
|  |  └── eval_forward_time.py # Test model inference time
|  |  └── eval_iou.py 
|  |  └── iouEval.py 
|  |  └── transform.py 

pip3 install -r requirements.txt

git clone https://github.com/xiaoyufenfei/LEDNet.git
cd LEDNet-master

├── leftImg8bit
│   ├── train
│   ├──  val
│   └── test
├── gtFine
│   ├── train
│   ├──  val
│   └── test
├── gtCoarse
│   ├── train
│   ├── train_extra
│   └── val

python main.py --savedir logs --model lednet --datadir path/root_directory/  --num-epochs xx --batch-size xx ...

python main.py --savedir logs --name lednet --datadir path/root_directory/  --num-epochs xx --batch-size xx --decoder --state "../save/logs/model_best_enc.pth.tar"...

more details refer ./test/README.md

 @article{wang2019lednet,
  title={LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation},
  author={Wang, Yu and Zhou, Quan and Liu, Jia and Xiong，Jian and Gao, Guangwei and Wu, Xiaofu, and Latecki Jan Longin},
  journal={arXiv preprint arXiv:1905.02423},
  year={2019}
}