PSPNet

Pyramid Scene Parsing Network

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

Introduction

This repository is for 'Pyramid Scene Parsing Network', which ranked 1st place in ImageNet Scene Parsing Challenge 2016. The code is modified from Caffe version of DeepLab v2 and yjxiong for evaluation. We merge the batch normalization layer named 'bn_layer' in the former one into the later one while keep the original 'batch_norm_layer' in the later one unchanged for compatibility. The difference is that 'bn_layer' contains four parameters as 'slope,bias,mean,variance' while 'batch_norm_layer' contains two parameters as 'mean,variance'. Several evaluation code is borrowed from MIT Scene Parsing.

PyTorch Version

Highly optimized PyTorch codebases available for semantic segmentation in repo: semseg, including full training and testing codes for PSPNet and PSANet.

Installation

For installation, please follow the instructions of Caffe and DeepLab v2. To enable cuDNN for GPU acceleration, cuDNN v4 is needed. If you meet error related with 'matio', please download and install matio as required in 'DeepLab v2'.

The code has been tested successfully on Ubuntu 14.04 and 12.04 with CUDA 7.0.

Usage

1. Clone the repository:

   bash复制

   git clone https://github.com/hszhao/PSPNet.git

2. Build Caffe and matcaffe:

   bash复制

   cd $PSPNET_ROOT
   cp Makefile.config.example Makefile.config
   vim Makefile.config
   make -j8 && make matcaffe

3. Evaluation:

   • Evaluation code is in folder 'evaluation'.
   • Download trained models and put them in folder 'evaluation/model':
     • pspnet50_ADE20K.caffemodel: GoogleDrive
     • pspnet101_VOC2012.caffemodel: GoogleDrive
     • pspnet101_cityscapes.caffemodel: GoogleDrive
   • Modify the related paths in 'eval_all.m':
     • Mainly variables 'data_root' and 'eval_list', and your image list for evaluation should be similarity to that in folder 'evaluation/samplelist' if you use this evaluation code structure.
     • Matlab 'parfor' evaluation is used and the default GPUs are with ID [0:3]. Modify variable 'gpu_id_array' if needed. We assume that number of images can be divided by number of GPUs; if not, you can just pad your image list or switch to single GPU evaluation by set 'gpu_id_array' be length of one, and change 'parfor' to 'for' loop.
   bash复制

   cd evaluation
   vim eval_all.m

   • Run the evaluation scripts:
   复制

   ./run.sh

4. Results:

   Prediction results will show in folder 'evaluation/mc_result' and the expected scores are:

   (single scale testing denotes as 'ss' and multiple scale testing denotes as 'ms')

   • PSPNet50 on ADE20K valset (mIoU/pAcc): 41.68/80.04 (ss) and 42.78/80.76 (ms)
   • PSPNet101 on VOC2012 testset (mIoU): 85.41 (ms)
   • PSPNet101 on cityscapes valset (mIoU/pAcc): 79.70/96.38 (ss) and 80.91/96.59 (ms)

5. Demo video:

   Video processed by PSPNet101 on cityscapes dataset:

   Merge with colormap on side: Video1

   Alpha blending with value as 0.5: Video2

Citation

If PSPNet is useful for your research, please consider citing:

@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}
}

Questions

Please contact '[email protected]'