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https://github.com/speedinghzl/pytorch-segmentation-toolbox
PyTorch Implementations for DeeplabV3 and PSPNet
https://github.com/speedinghzl/pytorch-segmentation-toolbox
cityscape-dataset deeplabv3 pspnet pytorch pytorch-segmentation-toolbox semantic-segmentation
Last synced: 15 days ago
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PyTorch Implementations for DeeplabV3 and PSPNet
- Host: GitHub
- URL: https://github.com/speedinghzl/pytorch-segmentation-toolbox
- Owner: speedinghzl
- License: mit
- Created: 2018-11-07T20:11:58.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-20T15:53:52.000Z (over 4 years ago)
- Last Synced: 2023-10-25T23:22:54.021Z (about 1 year ago)
- Topics: cityscape-dataset, deeplabv3, pspnet, pytorch, pytorch-segmentation-toolbox, semantic-segmentation
- Language: Python
- Homepage:
- Size: 448 KB
- Stars: 759
- Watchers: 15
- Forks: 165
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Pytorch-segmentation-toolbox [DOC](https://weiyc.github.io/assets/pdf/toolbox.pdf)
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.
**The new version toolbox is released on branch [Pytorch-1.1](https://github.com/speedinghzl/pytorch-segmentation-toolbox/tree/pytorch-1.1) which supports Pytorch 1.0 or later and distributed multiprocessing training and testing**
### 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:
```bash
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](http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth), and put it into `dataset` folder.
### Training and Evaluation
```bash
./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](https://github.com/speedinghzl/CCNet) and [Object Context Network(OCNet)](https://github.com/PkuRainBow/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)](https://github.com/liutinglt/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](https://github.com/mapillary/inplace_abn) -
[Pytorch-Deeplab](https://github.com/speedinghzl/Pytorch-Deeplab) -
[PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)