GitHub - speedinghzl/pytorch-segmentation-toolbox at pytorch-1.1 (original) (raw)
Pytorch-segmentation-toolbox Pytorch-1.1 DOC
Pytorch code for semantic segmentation. This is a minimal code to run PSPnet and Deeplabv3 on Cityscape dataset. Shortly afterwards, the code will be reviewed and reorganized for convenience.
Highlights of Our Implementations
- Synchronous BN
- Fewness of Training Time
- Better Reproduced Performance
Requirements && Install
Python 3.7
4 x 12g GPUs (e.g. TITAN XP)
Install Pytorch-1.1
$ conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
Install Apex
$ git clone https://github.com/NVIDIA/apex $ cd apex $ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
Install Inplace-ABN
$ git clone https://github.com/mapillary/inplace_abn.git $ cd inplace_abn $ python setup.py install
Dataset and pretrained model
Plesae download cityscapes dataset and unzip the dataset into YOUR_CS_PATH.
Please download MIT imagenet pretrained resnet101-imagenet.pth, and put it into dataset folder.
Training and Evaluation
./run_local.sh YOUR_CS_PATH [pspnet|deeplabv3] 40000 769,769 0
Benefits
Some recent projects have already benefited from our implementations. For example, CCNet: Criss-Cross Attention for semantic segmentation and Object Context Network(OCNet) currently achieve the state-of-the-art resultson Cityscapes and ADE20K. In addition, Our code also make great contributions to Context Embedding with EdgePerceiving (CE2P), which won the 1st places in all human parsing tracks in the 2nd LIP Challange.
Citing
If you find this code useful in your research, please consider citing:
@misc{huang2018torchseg,
author = {Huang, Zilong and Wei, Yunchao and Wang, Xinggang, and Liu, Wenyu},
title = {A PyTorch Semantic Segmentation Toolbox},
howpublished = {\url{https://github.com/speedinghzl/pytorch-segmentation-toolbox}},
year = {2018}
}