This repository is a PyTorch implementation for semantic segmentation / scene parsing. The code is easy to use for training and testing on various datasets. The codebase mainly uses ResNet50/101/152 as backbone and can be easily adapted to other basic classification structures. Implemented networks including PSPNet and PSANet, which ranked 1st places in ImageNet Scene Parsing Challenge 2016 @ECCV16, LSUN Semantic Segmentation Challenge 2017 @CVPR17 and WAD Drivable Area Segmentation Challenge 2018 @CVPR18. Sample experimented datasets are ADE20K, PASCAL VOC 2012 and Cityscapes.
master, use official nn.SyncBatchNorm, only multiprocessing training is supported, tested with pytorch 1.4.0.1.0.0, both multithreading training (nn.DataParallel) and multiprocessing training (nn.parallel.DistributedDataParallel) (recommended) are supported. And the later one is much faster. Use syncbn from EncNet and apex, tested with pytorch 1.0.0.Highlight:
Requirement:
Clone the repository:
git clone https://github.com/hszhao/semseg.git
Train:
Download related datasets and symlink the paths to them as follows (you can alternatively modify the relevant paths specified in folder config):
cd semseg
mkdir -p dataset
ln -s /path_to_ade20k_dataset dataset/ade20k
Download ImageNet pre-trained models and put them under folder initmodel for weight initialization. Remember to use the right dataset format detailed in FAQ.md.
Specify the gpu used in config then do training:
sh tool/train.sh ade20k pspnet50
If you are using SLURM for nodes manager, uncomment lines in train.sh and then do training:
sbatch tool/train.sh ade20k pspnet50
Test:
Download trained segmentation models and put them under folder specified in config or modify the specified paths.
For full testing (get listed performance):
sh tool/test.sh ade20k pspnet50
Quick demo on one image:
PYTHONPATH=./ python tool/demo.py --config=config/ade20k/ade20k_pspnet50.yaml --image=figure/demo/ADE_val_00001515.jpg TEST.scales '[1.0]'
Visualization: tensorboardX incorporated for better visualization.
tensorboard --logdir=exp/ade20k
Other:
names and colors) are in folder dataset and some sample lists can be accessed.Description: mIoU/mAcc/aAcc stands for mean IoU, mean accuracy of each class and all pixel accuracy respectively. ss denotes single scale testing and ms indicates multi-scale testing. Training time is measured on a sever with 8 GeForce RTX 2080 Ti. General parameters cross different datasets are listed below:
ADE20K: Train Parameters: classes(150), train_h(473/465-PSP/A), train_w(473/465-PSP/A), epochs(100). Test Parameters: classes(150), test_h(473/465-PSP/A), test_w(473/465-PSP/A), base_size(512).
PSACAL VOC 2012: Train Parameters: classes(21), train_h(473/465-PSP/A), train_w(473/465-PSP/A), epochs(50). Test Parameters: classes(21), test_h(473/465-PSP/A), test_w(473/465-PSP/A), base_size(512).
Cityscapes: Train Parameters: classes(19), train_h(713/709-PSP/A), train_w(713/709-PSP/A), epochs(200). Test Parameters: classes(19), test_h(713/709-PSP/A), test_w(713/709-PSP/A), base_size(2048).
If you find the code or trained models useful, please consider citing:
@misc{semseg2019,
author={Zhao, Hengshuang},
title={semseg},
howpublished={\url{https://github.com/hszhao/semseg}},
year={2019}
}
@inproceedings{zhao2017pspnet,
title={Pyramid Scene Parsing Network},
author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
booktitle={CVPR},
year={2017}
}
@inproceedings{zhao2018psanet,
title={{PSANet}: Point-wise Spatial Attention Network for Scene Parsing},
author={Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Loy, Chen Change and Lin, Dahua and Jia, Jiaya},
booktitle={ECCV},
year={2018}
}
Some FAQ.md collected. You are welcome to send pull requests or give some advices. Contact information: hengshuangzhao at gmail.com.
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