CRF-RNN for Semantic Image Segmentation - PyTorch version

Live demo:                           http://crfasrnn.torr.vision
Caffe version:                      http://github.com/torrvision/crfasrnn
Tensorflow/Keras version: http://github.com/sadeepj/crfasrnn_keras

This repository contains the official PyTorch implementation of the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. The online demo of this project won the Best Demo Prize at ICCV 2015. Results of this PyTorch code are identical to that of the Caffe and Tensorflow/Keras based versions above.

If you use this code/model for your research, please cite the following paper:

@inproceedings{crfasrnn_ICCV2015,
    author = {Shuai Zheng and Sadeep Jayasumana and Bernardino Romera-Paredes and Vibhav Vineet and
    Zhizhong Su and Dalong Du and Chang Huang and Philip H. S. Torr},
    title  = {Conditional Random Fields as Recurrent Neural Networks},
    booktitle = {International Conference on Computer Vision (ICCV)},
    year   = {2015}
}

Installation Guide

Note: If you are using a Python virtualenv, make sure it is activated before running each command in this guide.

Step 1: Clone the repository

$ git clone https://github.com/sadeepj/crfasrnn_pytorch.git

The root directory of the clone will be referred to as crfasrnn_pytorch hereafter.

Step 2: Install dependencies

Use the requirements.txt file in this repository to install all the dependencies via pip:

$ cd crfasrnn_pytorch
$ pip install -r requirements.txt

After installing the dependencies, run the following commands to make sure they are properly installed:

$ python
>>> import torch 

You should not see any errors while importing torch above.

Step 3: Build CRF-RNN custom op

Run setup.py inside the crfasrnn_pytorch/crfasrnn directory:

$ cd crfasrnn_pytorch/crfasrnn
$ python setup.py install 

Note that the python command in the console should refer to the Python interpreter associated with your PyTorch installation.

Step 4: Download the pre-trained model weights

Download the model weights from here and place it in the crfasrnn_pytorch directory with the file name crfasrnn_weights.pth.

Step 5: Run the demo

$ cd crfasrnn_pytorch
$ python run_demo.py

If all goes well, you will see the segmentation results in a file named "labels.png".

Contributors

• Sadeep Jayasumana (sadeepj)
• Harsha Ranasinghe (HarshaPrabhath)