Pytorch-segmentation-toolbox 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

To install PyTorch>=0.4.0, please refer to https://github.com/pytorch/pytorch#installation.

4 x 12g GPUs (e.g. TITAN XP)

Python 3.6

Compiling

Some parts of InPlace-ABN have a native CUDA implementation, which must be compiled with the following commands:

cd libs
sh build.sh
python build.py

The build.sh script assumes that the nvcc compiler is available in the current system search path. The CUDA kernels are compiled for sm_50, sm_52 and sm_61 by default. To change this (e.g. if you are using a Kepler GPU), please edit the CUDA_GENCODE variable in build.sh.

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

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}
}

Thanks to the Third Party Libs

inplace_abn - Pytorch-Deeplab - PyTorch-Encoding