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}
}

Thanks to the Third Party Libs

inplace_abn - Pytorch-Deeplab - PyTorch-Encoding