CRF-RNN for Semantic Image Segmentation - Keras/Tensorflow version

Live demo: http://crfasrnn.torr.vision
Caffe version: http://github.com/torrvision/crfasrnn

This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version.

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

1.1 Install dependencies

Install Tensorflow and Keras, following the respective installation guides. You will need to install Keras with HDF5/h5py if you plan to use the provided trained model. After installing these two packages, run the following commands to make sure they are properly installed:

# First, activate the correct Python virtualenv if you used one during Tensorflow/Keras installation
$ source /home/user/tensorflow_virtualenv/bin/activate  
$ python
>>> import tensorflow
>>> import keras

You should not see any errors while importing tensorflow and keras above.

1.2 Build CRF-RNN custom C++ code

Checkout the code in this repository, activate the Tensorflow/Keras virtualenv (if you used one), and run the compile.sh script in the cpp directory. That is, run the following commands:

$ git clone https://github.com/sadeepj/crfasrnn_keras.git
$ cd crfasrnn_keras/cpp
$ source /home/user/tensorflow_virtualenv/bin/activate
$ ./compile.sh

If the build succeeds, you will see a new file named high_dim_filter.so. If it fails, please see the comments inside the compile.sh file for help. You could also refer to the official Tensorflow guide for building a custom op.

Note: This script will not work on Windows OS. If you are on Windows, please check this issue and the comments therein. The official Tensorflow guide for building a custom op does not yet include build instructions for Windows.

1.3 Download the pre-trained model weights

Download the model weights from here and place it in the crfasrnn_keras directory with the file name crfrnn_keras_model.h5.

1.4 Run the demo

$ cd crfasrnn_keras
$ python run_demo.py  # Make sure that the correct virtualenv is already activated

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

Limitations of the Current Version

Currently, some operations in the CRF-RNN layer can only run on the CPU. An all-GPU version will be released soon.
The crfrnn_keras_model.h5 model was directly converted from the Caffe model. However, training new models entirely from Keras is possible too.

sirotenko/crfasrnn_keras